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z7upJ$7mPmYz)z)jU)Ic{B{;>>Fkr)J9tHIImnAe$IR?sQYzAnhOaMGFf~8a>%E hVM8lN8abXLmZ3N*pLDgcNk`xQ>?s=y+6m_y{{nyq&`tmV literal 0 HcmV?d00001 From 3bc04a0080013872076c6f523f02f8556f82aa9d Mon Sep 17 00:00:00 2001 From: Francis Charette-Migneault Date: Tue, 29 Oct 2024 15:32:26 -0400 Subject: [PATCH 02/83] Update README.md with wording about band name references --- README.md | 5 ++--- 1 file changed, 2 insertions(+), 3 deletions(-) diff --git a/README.md b/README.md index 62feab4..dce0f89 100644 --- a/README.md +++ b/README.md @@ -297,10 +297,9 @@ and [Common Band Names][stac-band-names]. > Therefore, `eo:bands` should either be specified *only* under the Asset containing the `mlm:model` role > (see [Model Asset](#model-asset)), or define them *both* under the Asset and Item properties. If the second > approach is selected, it is recommended that the `eo:bands` under the Asset contains only the `name` or the -> `common_name` property, such that all other details about the bands are defined at the Item level. -> An example of such representation is provided in +> `common_name` property, such that all other details about the bands are defined and cross-referenced by name +> with the Item-level band definitions. An example of such representation is provided in > [examples/item_eo_bands_summarized.json](examples/item_eo_bands_summarized.json). ->

> For an example where `eo:bands` are entirely defined in the Asset on their own, please refer to > [examples/item_eo_bands.json](examples/item_eo_bands.json) instead. >

From 42f0a9050b1dbfc8700652f97c79dd3451faebfd Mon Sep 17 00:00:00 2001 From: Francis Charette Migneault Date: Wed, 30 Oct 2024 10:00:01 -0400 Subject: [PATCH 03/83] [wip] add check for matching norm-type/statistics mlm:input properties --- json-schema/schema.json | 41 ++++++++++++++++++++++++++++++++++++++++- tests/test_schema.py | 28 ++++++++++++++++++++++++++++ 2 files changed, 68 insertions(+), 1 deletion(-) diff --git a/json-schema/schema.json b/json-schema/schema.json index ff21357..4fa86e7 100644 --- a/json-schema/schema.json +++ b/json-schema/schema.json @@ -490,6 +490,13 @@ "mlm:input": { "type": "array", "items": { + "allOf": [ + {"$ref": "#/$defs/ModelInput"}, + {"$ref": "#/$defs/ModelInputNormTypeStatistics"} + ] + } + }, + "ModelInput": { "title": "Model Input Object", "type": "object", "required": [ @@ -531,7 +538,39 @@ "$ref": "#/$defs/ProcessingExpression" } } - } + }, + "ModelInputNormTypeStatistics": { + "description": "Validates the corresponding statistics required for the specified normalization type.", + "oneOf": [ + {"type": "object", "properties": {"norm_type": {"type": "null"}}}, + { + "if": { + "type": "object", + "required": ["norm_type"], + "properties": { + "norm_type": { + "const": "min-max" + } + } + }, + "then": { + "type": "object", + "required": ["statistics"], + "properties": { + "statistics": { + "allOf": [ + { + "$ref": "#/$defs/InputStatistics" + }, + { + "items": {"required": ["minimum", "maximum"]} + } + ] + } + } + } + } + ] }, "mlm:output": { "type": "array", diff --git a/tests/test_schema.py b/tests/test_schema.py index 4755978..6c3ddc0 100644 --- a/tests/test_schema.py +++ b/tests/test_schema.py @@ -85,6 +85,34 @@ def test_mlm_no_input_allowed_but_explicit_empty_array_required( pystac.validation.validate(mlm_item, validator=mlm_validator) +@pytest.mark.parametrize( + "mlm_example", + ["item_basic.json"], + indirect=True, +) +@pytest.mark.parametrize( + ["test_norm_type", "test_statistics"], + [ + ("min-max", [{"mean": 1, "stddev": 2}]) + ], +) +def test_mlm_invalid_input_norm_type_statistics_combination( + mlm_validator: STACValidator, + mlm_example: Dict[str, JSON], + test_norm_type: str, + test_statistics: List[Dict[str, Any]], +) -> None: + mlm_data = copy.deepcopy(mlm_example) + mlm_item = pystac.Item.from_dict(mlm_data) + pystac.validation.validate(mlm_item, validator=mlm_validator) # ensure original is valid + + with pytest.raises(pystac.errors.STACValidationError): + mlm_data["properties"]["mlm:input"][0]["norm_type"] = test_norm_type + mlm_data["properties"]["mlm:input"][0]["statistics"] = test_statistics + mlm_item = pystac.Item.from_dict(mlm_data) + pystac.validation.validate(mlm_item, validator=mlm_validator) + + @pytest.mark.parametrize( "mlm_example", ["item_basic.json"], From c5f243e7f0e47f74061771630d0f2498d809c74d Mon Sep 17 00:00:00 2001 From: Ryan Avery Date: Wed, 30 Oct 2024 13:19:47 -0400 Subject: [PATCH 04/83] break out part of description to Resources section, add open paper link and presentation pdf --- README.md | 4 ++++ docs/static/sigspatial_2024_mlm.pdf | Bin 0 -> 1315778 bytes 2 files changed, 4 insertions(+) create mode 100644 docs/static/sigspatial_2024_mlm.pdf diff --git a/README.md b/README.md index dce0f89..ad10344 100644 --- a/README.md +++ b/README.md @@ -91,6 +91,8 @@ For more details about the [`stac-model`](./stac_model) Python package, which pr using both [`Pydantic`](https://docs.pydantic.dev/latest/) and [`PySTAC`](https://pystac.readthedocs.io/en/stable/) connectors, please refer to the [STAC Model](./README_STAC_MODEL.md) document. +## Resources + - Examples: - [Item examples](https://huggingface.co/wherobots/mlm-stac) for scene-classification, object detection, and semantic segmentation: Shows real world use of the @@ -99,6 +101,8 @@ connectors, please refer to the [STAC Model](./README_STAC_MODEL.md) document. - [Collection example](examples/collection.json): Shows the basic usage of the extension in a STAC Collection - [JSON Schema](https://stac-extensions.github.io/mlm/) - [Changelog](./CHANGELOG.md) +- [Open access paper](https://dl.acm.org/doi/10.1145/3681769.3698586) describing version 1.3.0 of the extension +- [SigSpatial 2024 GeoSearch Workshop presentation](/docs/static/sigspatial_2024_mlm.pdf) ## Item Properties and Collection Fields diff --git a/docs/static/sigspatial_2024_mlm.pdf b/docs/static/sigspatial_2024_mlm.pdf new file mode 100644 index 0000000000000000000000000000000000000000..9e76478f3bdb34ae956f99e2808ff86645158f83 GIT binary patch literal 1315778 zcmeFY1yo$y)+Sm64G`Sjo!~)2AOt74JHg#uD?x(?2qd_>h2S3CT?4`0-HW0(x%d3{ zobz|zet(aCuSbvZsIhjj_g;IiIoFzN&TmT5DvC+4ay;iorlnY28<{}nLS{#{H?l$& z6hvlI^>i>rW)n4ZF|@HaM`n{Zv^8~pydfln%%*5)ZYpbNWNP#K3Y)yEtuwL?@a*Gr zD#&bNz|jSnXEM06&e_xja$%5oS6d-Ys2gn6v3bF$^gDiobcEB|i;6HQV z=m0VV?pXpy8xRHKp9bUk(_sH#^^vJZ(wt1~9*O@=sGO;ZrQsWUkKf#}BlB{yBXhI! z09>(&+S|DR9i5S1{G+F`sk6PSlQF;}?>{bx*xA_wxB35ZCh27F>hQ<>s5%+iIXgTK 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z-XrScK-M(`hAdMAhO8R^24h3+Gk_uO@rX8H?8q_zFy!?BL5Do%00x`@k>mnk5TtpD zphG$XBie-{?cF~y Date: Wed, 30 Oct 2024 14:06:58 -0400 Subject: [PATCH 05/83] add nom-type vs statistics checks for specific combinations --- json-schema/schema.json | 144 +++++++++++++++++++++++++++------------- tests/test_schema.py | 3 +- 2 files changed, 99 insertions(+), 48 deletions(-) diff --git a/json-schema/schema.json b/json-schema/schema.json index 4fa86e7..1ca916d 100644 --- a/json-schema/schema.json +++ b/json-schema/schema.json @@ -491,62 +491,67 @@ "type": "array", "items": { "allOf": [ - {"$ref": "#/$defs/ModelInput"}, - {"$ref": "#/$defs/ModelInputNormTypeStatistics"} + { + "$ref": "#/$defs/ModelInput" + }, + { + "$ref": "#/$defs/ModelInputNormTypeStatistics" + } ] } }, "ModelInput": { - "title": "Model Input Object", - "type": "object", - "required": [ - "name", - "bands", - "input" - ], - "properties": { - "name": { - "type": "string", - "minLength": 1 - }, - "bands": { - "$ref": "#/$defs/ModelBands" - }, - "input": { - "$ref": "#/$defs/InputStructure" - }, - "description": { - "type": "string", - "minLength": 1 - }, - "norm_by_channel": { - "type": "boolean" - }, - "norm_type": { - "$ref": "#/$defs/NormalizeType" - }, - "norm_clip": { - "$ref": "#/$defs/NormalizeClip" - }, - "resize_type": { - "$ref": "#/$defs/ResizeType" - }, - "statistics": { - "$ref": "#/$defs/InputStatistics" - }, - "pre_processing_function": { - "$ref": "#/$defs/ProcessingExpression" - } + "title": "Model Input Object", + "type": "object", + "required": [ + "name", + "bands", + "input" + ], + "properties": { + "name": { + "type": "string", + "minLength": 1 + }, + "bands": { + "$ref": "#/$defs/ModelBands" + }, + "input": { + "$ref": "#/$defs/InputStructure" + }, + "description": { + "type": "string", + "minLength": 1 + }, + "norm_by_channel": { + "type": "boolean" + }, + "norm_type": { + "$ref": "#/$defs/NormalizeType" + }, + "norm_clip": { + "$ref": "#/$defs/NormalizeClip" + }, + "resize_type": { + "$ref": "#/$defs/ResizeType" + }, + "statistics": { + "$ref": "#/$defs/InputStatistics" + }, + "pre_processing_function": { + "$ref": "#/$defs/ProcessingExpression" } + } }, "ModelInputNormTypeStatistics": { "description": "Validates the corresponding statistics required for the specified normalization type.", - "oneOf": [ - {"type": "object", "properties": {"norm_type": {"type": "null"}}}, + "allOf": [ { "if": { "type": "object", - "required": ["norm_type"], + "required": [ + "norm_type" + ], "properties": { "norm_type": { "const": "min-max" @@ -555,7 +560,46 @@ }, "then": { "type": "object", - "required": ["statistics"], + "required": [ + "statistics" + ], + "properties": { + "statistics": { + "allOf": [ + { + "$ref": "#/$defs/InputStatistics" + }, + { + "type": "array", + "items": { + "required": [ + "minimum", + "maximum" + ] + } + } + ] + } + } + } + }, + { + "if": { + "type": "object", + "required": [ + "norm_type" + ], + "properties": { + "norm_type": { + "const": "z-score" + } + } + }, + "then": { + "type": "object", + "required": [ + "statistics" + ], "properties": { "statistics": { "allOf": [ @@ -563,7 +607,13 @@ "$ref": "#/$defs/InputStatistics" }, { - "items": {"required": ["minimum", "maximum"]} + "type": "array", + "items": { + "required": [ + "mean", + "stddev" + ] + } } ] } diff --git a/tests/test_schema.py b/tests/test_schema.py index 6c3ddc0..9c356f3 100644 --- a/tests/test_schema.py +++ b/tests/test_schema.py @@ -93,7 +93,8 @@ def test_mlm_no_input_allowed_but_explicit_empty_array_required( @pytest.mark.parametrize( ["test_norm_type", "test_statistics"], [ - ("min-max", [{"mean": 1, "stddev": 2}]) + ("min-max", [{"mean": 1, "stddev": 2}]), + ("z-score", [{"minimum": 1, "maximum": 2}]), ], ) def test_mlm_invalid_input_norm_type_statistics_combination( From d2cfbeb1d24051b998af2135b6b647f0eaae792c Mon Sep 17 00:00:00 2001 From: Francis Charette Migneault Date: Wed, 30 Oct 2024 14:47:31 -0400 Subject: [PATCH 06/83] test more norm_type/statistics combinations --- CHANGELOG.md | 4 +++- tests/test_schema.py | 25 +++++++++++++++++-------- 2 files changed, 20 insertions(+), 9 deletions(-) diff --git a/CHANGELOG.md b/CHANGELOG.md index 116e1d3..4651778 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -8,7 +8,9 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0 ## [Unreleased](https://github.com/stac-extensions/mlm/tree/main) ### Added -- n/a +- Add explicit check of `statitics` sub-fields `minimum`, `maximum`, `mean` and `stddev` for corresponding `norm_type` + values `min-max` and `z-score` that depend on it. Other `norm_type` values are ignored. Additional `statistics` + properties not directly required by the specific `norm_type` are also ignored. ### Changed - n/a diff --git a/tests/test_schema.py b/tests/test_schema.py index 9c356f3..b76e83c 100644 --- a/tests/test_schema.py +++ b/tests/test_schema.py @@ -91,27 +91,36 @@ def test_mlm_no_input_allowed_but_explicit_empty_array_required( indirect=True, ) @pytest.mark.parametrize( - ["test_norm_type", "test_statistics"], + ["test_norm_type", "test_statistics", "is_valid"], [ - ("min-max", [{"mean": 1, "stddev": 2}]), - ("z-score", [{"minimum": 1, "maximum": 2}]), + ("min-max", [{"mean": 1, "stddev": 2}], False), + ("z-score", [{"minimum": 1, "maximum": 2}], False), + ("min-max", [{"mean": 1, "stddev": 2}, {"minimum": 1, "maximum": 2}], False), + ("z-score", [{"mean": 1, "stddev": 2}, {"minimum": 1, "maximum": 2}], False), + ("min-max", [{"minimum": 1, "maximum": 2}], True), + ("z-score", [{"mean": 1, "stddev": 2, "minimum": 1, "maximum": 2}], True), # extra statistics must be ignored + ("l2", [{"mean": 1, "stddev": 2}], True), # statistics "don't" care for this norm-type ], ) -def test_mlm_invalid_input_norm_type_statistics_combination( +def test_mlm_input_norm_type_statistics_combination( mlm_validator: STACValidator, mlm_example: Dict[str, JSON], test_norm_type: str, test_statistics: List[Dict[str, Any]], + is_valid: bool, ) -> None: mlm_data = copy.deepcopy(mlm_example) mlm_item = pystac.Item.from_dict(mlm_data) pystac.validation.validate(mlm_item, validator=mlm_validator) # ensure original is valid - with pytest.raises(pystac.errors.STACValidationError): - mlm_data["properties"]["mlm:input"][0]["norm_type"] = test_norm_type - mlm_data["properties"]["mlm:input"][0]["statistics"] = test_statistics - mlm_item = pystac.Item.from_dict(mlm_data) + mlm_data["properties"]["mlm:input"][0]["norm_type"] = test_norm_type + mlm_data["properties"]["mlm:input"][0]["statistics"] = test_statistics + mlm_item = pystac.Item.from_dict(mlm_data) + if is_valid: pystac.validation.validate(mlm_item, validator=mlm_validator) + else: + with pytest.raises(pystac.errors.STACValidationError): + pystac.validation.validate(mlm_item, validator=mlm_validator) @pytest.mark.parametrize( From 15b293cf90076f782242b33d5f1ce6cd94e96957 Mon Sep 17 00:00:00 2001 From: Ryan Avery Date: Wed, 30 Oct 2024 14:35:14 -0700 Subject: [PATCH 07/83] wip adding tensorflow artifact types, remove torch.compile --- README.md | 15 +++++++++++---- 1 file changed, 11 insertions(+), 4 deletions(-) diff --git a/README.md b/README.md index ad10344..423139f 100644 --- a/README.md +++ b/README.md @@ -657,10 +657,12 @@ the users understand the source explicitly, although this is not strictly requir | Artifact Type | Description | |--------------------|--------------------------------------------------------------------------------------| -| `torch.save` | A model artifact obtained by [Serialized Pickle Object][pytorch-save] (i.e.: `.pt`). | -| `torch.jit.script` | A model artifact obtained by [`TorchScript`][pytorch-jit-script]. | -| `torch.export` | A model artifact obtained by [`torch.export`][pytorch-export] (i.e.: `.pt2`). | -| `torch.compile` | A model artifact obtained by [`torch.compile`][pytorch-compile]. | +| `torch.save` | A [serialized python pickle object][pytorch-save] (i.e.: `.pt`) which can represent a model or state_dict. | +| `torch.jit.save` | A [`TorchScript`][pytorch-jit-script] model artifact obtained with one or more of the graph export options Torchscript Tracing and Torchscript Scripting. | +| `torch.export.save` | A model artifact storing an ExportedProgram obtained by [`torch.export.export`][pytorch-export] (i.e.: `.pt2`). | +| `TFSavedModel` | A [SavedModel][tf-save] from Tensorflow or Keras. | +| `Keras_v3` | Keras v3 is the [recommended format][keras-recommended] by the Tensorflow team. See this example to [save and load models][keras-example] and the update to date docs [disambiguating different save methods][keras-methods] in TF and Keras.. | +| `h5` | [Keras and tf.keras model][h5] weights format, which uses HDF5. | [pytorch-compile]: https://pytorch.org/tutorials/intermediate/torch_compile_tutorial.html [pytorch-export]: https://pytorch.org/docs/main/export.html @@ -668,6 +670,11 @@ the users understand the source explicitly, although this is not strictly requir [pytorch-aot-inductor]: https://pytorch.org/docs/main/torch.compiler_aot_inductor.html [pytorch-jit-script]: https://pytorch.org/docs/stable/jit.html [pytorch-save]: https://pytorch.org/tutorials/beginner/saving_loading_models.html +[tf-save]: https://www.tensorflow.org/guide/saved_model +[keras-example]: https://keras.io/guides/serialization_and_saving/ +[keras-recommended]: https://www.tensorflow.org/guide/saved_model#creating_a_savedmodel_from_keras +[h5]: https://keras.io/api/models/model_saving_apis/weights_saving_and_loading/ +[keras-methods]: https://keras.io/2.16/api/models/model_saving_apis/model_saving_and_loading/ ### Source Code Asset From 7635f0363ea20673ff877801e7f513fbe868d627 Mon Sep 17 00:00:00 2001 From: Ryan Avery Date: Thu, 31 Oct 2024 13:53:05 -0400 Subject: [PATCH 08/83] convert poetry toml to uv toml using pdm build backend --- pyproject.toml | 151 +++--- uv.lock | 1347 ++++++++++++++++++++++++++++++++++++++++++++++++ 2 files changed, 1416 insertions(+), 82 deletions(-) create mode 100644 uv.lock diff --git a/pyproject.toml b/pyproject.toml index f76d9fe..546754d 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -1,93 +1,84 @@ - -# Poetry pyproject.toml: https://python-poetry.org/docs/pyproject/ [build-system] -requires = ["poetry_core>=1.0.0"] -build-backend = "poetry.core.masonry.api" +requires = ["pdm-backend"] +build-backend = "pdm.backend" +[tool.pdm.build] +includes = [ + "stac_model", +] -[tool.poetry] +[project] +authors = [ + {name = "Ryan Avery", email = "ryan@wherobots.com"}, + {name = "Francis Charette-Migneault", email = "francis.charette-migneault@crim.ca"}, +] +license = {text = "Apache Software License 2.0"} +requires-python = "<4.0,>=3.10" +dependencies = [ + "typer[all]<1.0.0,>=0.9.0", + "rich<14.0.0,>=13.7.0", + "pydantic<3.0.0,>=2.6.3", + "pydantic-core<3,>=2", + "pystac<2.0.0,>=1.9.0", + "shapely<3,>=2", + "jsonschema<5.0.0,>=4.21.1", +] name = "stac-model" version = "0.2.0" description = "A PydanticV2 validation and serialization libary for the STAC ML Model Extension" readme = "README_STAC_MODEL.md" -authors = [ - "Ryan Avery ", - "Francis Charette-Migneault " -] -license = "Apache Software License 2.0" -repository = "https://github.com/crim-ca/mlm-extension" -homepage = "https://github.com/stac-extensions/mlm/blob/main/README_STAC_MODEL.md" -packages = [ - {include = "stac_model"} -] - - -# Keywords description https://python-poetry.org/docs/pyproject/#keywords -keywords = [] # UPDATEME with relevant keywords - - -# Pypi classifiers: https://pypi.org/classifiers/ +keywords = [] classifiers = [ - "Development Status :: 4 - Beta", - "Operating System :: OS Independent", - "Topic :: Software Development :: Libraries :: Python Modules", - "License :: OSI Approved :: Apache Software License", - "Programming Language :: Python :: 3", - "Programming Language :: Python :: 3.10", - "Programming Language :: Python :: 3.11", - "Programming Language :: Python :: 3.12", - "Programming Language :: Python :: 3 :: Only", - "Framework :: Pydantic", - "Framework :: Pydantic :: 2", - "Intended Audience :: Developers", - "Intended Audience :: Information Technology", - "Intended Audience :: Science/Research", - "Topic :: File Formats :: JSON :: JSON Schema", - "Topic :: Scientific/Engineering :: Artificial Intelligence", - "Topic :: Scientific/Engineering :: GIS", - "Topic :: Scientific/Engineering :: Image Processing", - "Topic :: Scientific/Engineering :: Image Recognition", + "Development Status :: 4 - Beta", + "Operating System :: OS Independent", + "Topic :: Software Development :: Libraries :: Python Modules", + "License :: OSI Approved :: Apache Software License", + "Programming Language :: Python :: 3", + "Programming Language :: Python :: 3.10", + "Programming Language :: Python :: 3.11", + "Programming Language :: Python :: 3.12", + "Programming Language :: Python :: 3 :: Only", + "Framework :: Pydantic", + "Framework :: Pydantic :: 2", + "Intended Audience :: Developers", + "Intended Audience :: Information Technology", + "Intended Audience :: Science/Research", + "Topic :: File Formats :: JSON :: JSON Schema", + "Topic :: Scientific/Engineering :: Artificial Intelligence", + "Topic :: Scientific/Engineering :: GIS", + "Topic :: Scientific/Engineering :: Image Processing", + "Topic :: Scientific/Engineering :: Image Recognition", ] +[tool.uv] +dev-dependencies = [ + "mypy<2.0.0,>=1.0.0", + "mypy-extensions<1.0.0,>=0.4.3", + "pre-commit<3.0.0,>=2.21.0", + "bandit<2.0.0,>=1.7.5", + "safety<3.0.0,>=2.3.4", + "pystac<2.0.0,>=1.10.0", + "pydocstyle[toml]<7.0.0,>=6.2.0", + "pydoclint<0.6,>=0.3", + "pytest<8.0.0,>=7.2.1", + "pytest-cov<5.0.0,>=4.1.0", + "pytest-mock<4.0.0,>=3.10.0", + "pytest-timeout<3.0.0,>=2.2.0", + "pytest-benchmark<5.0.0,>=4.0.0", + "pytest-sugar<1.0.0,>=0.9.7", + "pytest-click<2.0.0,>=1.1.0", + "pytest-pikachu<2.0.0,>=1.0.0", + "coverage<8.0.0,>=7.3.0", + "ruff<1.0.0,>=0.2.2", + "bump-my-version<0.27,>=0.21", +] -[tool.poetry.scripts] -# Entry points for the package https://python-poetry.org/docs/pyproject/#scripts -"stac-model" = "stac_model.__main__:app" - - -[tool.poetry.dependencies] -python = "^3.10" - -typer = {extras = ["all"], version = "^0.9.0"} -rich = "^13.7.0" -pydantic = "^2.6.3" # bug in post 2.3 https://github.com/pydantic/pydantic/issues/7720 -pydantic-core = "^2" -pystac = "^1.9.0" -shapely = "^2" -jsonschema = "^4.21.1" - -[tool.poetry.group.dev.dependencies] -mypy = 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curl -sSL https://install.python-poetry.org | $(PYTHON) - --uninstall - -.PHONY: poetry-plugins -poetry-plugins: - $(POETRY) self add poetry-plugin-up - -.PHONY: poetry-env -poetry-env: - $(POETRY) config virtualenvs.in-project true +ACTIVEPYTHON = $(shell which python) + +#* UV +.PHONY: setup +setup: + @if ! command -v uv &> /dev/null; then \ + curl -LsSf https://astral.sh/uv/install.sh | sh; \ + else \ + echo "uv is already installed"; \ + fi + +.PHONY: remove +remove: + @read -p "Are you sure you want to remove uv and all its data? [y/N] " confirm && \ + if [ "$$confirm" = "y" ]; then \ + uv cache clean; \ + rm -r "$$(uv python dir)"; \ + rm -r "$$(uv tool dir)"; \ + rm ~/.cargo/bin/uv ~/.cargo/bin/uvx; \ + else \ + echo "Operation cancelled."; \ + fi .PHONY: publish publish: - $(POETRY) publish --build + uv publish --build #* Installation .PHONY: install -install: poetry-env - $(POETRY) lock -n && poetry export --without-hashes > requirements-lock.txt - $(POETRY) install -n - -poetry run mypy --install-types --non-interactive ./ +install: setup + uv export --format requirements-txt -o requirements.txt --no-dev + uv pip install --python $(ACTIVEPYTHON) -r requirements.txt .PHONY: install-dev -install-dev: poetry-env install - $(POETRY) install -n --with dev +install-dev: setup + uv export --format requirements-txt -o requirements-dev.txt + uv pip install --python $(ACTIVEPYTHON) -r requirements-dev.txt .PHONY: pre-commit-install -pre-commit-install: - $(POETRY) run pre-commit install - +pre-commit-install: setup + uv run --python $(ACTIVEPYTHON) pre-commit install #* Formatters .PHONY: codestyle -codestyle: - $(POETRY) run ruff format --config=pyproject.toml stac_model tests +codestyle: setup + uv run --python $(ACTIVEPYTHON) ruff format --config=pyproject.toml stac_model tests .PHONY: format format: codestyle #* Linting .PHONY: test -test: - PYTHONPATH=$(PYTHONPATH) poetry run pytest -c pyproject.toml --cov-report=html --cov=stac_model tests/ +test: setup + uv run --python $(ACTIVEPYTHON) pytest -c pyproject.toml --cov-report=html --cov=stac_model tests/ .PHONY: check check: check-examples check-markdown check-lint check-mypy check-safety check-citation @@ -61,8 +62,8 @@ check: check-examples check-markdown check-lint check-mypy check-safety check-ci check-all: check .PHONY: mypy -mypy: - $(POETRY) run mypy --config-file pyproject.toml ./ +mypy: setup + uv run --python $(ACTIVEPYTHON) mypy --config-file pyproject.toml ./ .PHONY: check-mypy check-mypy: mypy @@ -72,26 +73,25 @@ check-mypy: mypy # see https://github.com/citation-file-format/cffconvert/issues/292 .PHONY: check-citation check-citation: - docker run --rm -v $(PYTHONPATH)/CITATION.cff:/app/CITATION.cff citationcff/cffconvert --validate + docker run --rm -v $(ACTIVEPYTHON)/CITATION.cff:/app/CITATION.cff citationcff/cffconvert --validate .PHONY: check-safety -check-safety: - $(POETRY) check - $(POETRY) run safety check --full-report - $(POETRY) run bandit -ll --recursive stac_model tests +check-safety: setup + uv check + uv run --python $(ACTIVEPYTHON) safety check --full-report + uv run --python $(ACTIVEPYTHON) bandit -ll --recursive stac_model tests .PHONY: lint -lint: - $(POETRY) run ruff --config=pyproject.toml ./ - $(POETRY) run pydocstyle --count --config=pyproject.toml ./ - $(POETRY) run pydoclint --config=pyproject.toml ./ +lint: setup + uv run --python $(ACTIVEPYTHON) ruff check --fix --config=pyproject.toml ./ .PHONY: check-lint check-lint: lint +uv run --python $(ACTIVEPYTHON) ruff check --config=pyproject.toml ./ .PHONY: format-lint -format-lint: - $(POETRY) run ruff --config=pyproject.toml --fix ./ +format-lint: setup lint + ruff format --config=pyproject.toml ./ .PHONY: install-npm install-npm: @@ -120,8 +120,9 @@ $(addprefix fix-, $(FORMATTERS)): fix-%: format-% lint-all: lint mypy check-safety check-markdown .PHONY: update-dev-deps -update-dev-deps: - 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values `min-max` and `z-score` that depend on it. Other `norm_type` values are ignored. Additional `statistics` - properties not directly required by the specific `norm_type` are also ignored. +- Add explicit check of `scaling` sub-fields `minimum`, `maximum`, `mean`, `stddev`, etc. for corresponding `type` + values `min-max` and `z-score` that depend on it. +- Allow different `scaling` operations per band/channel/dimension as needed by the model. +- Allow a `processing:expression` for a band/channel/dimension-specific `scaling` operation, granting more flexibility + in the definition of input preparation in contrast to having it applied for the entire input (but still possible). ### Changed -- n/a +- Moved `norm_type` to `scaling` object to better reflect the expected operation, which could be another operation + than what is typically known as "normalization" or "standardization" techniques in machine learning. +- Moved `statistics` to `scaling` object to better reflect their mutual `type` and additional properties dependencies. ### Deprecated - n/a ### Removed -- n/a +- Removed `norm_type` enum values that were ambiguous regarding their expected result. + Instead, a `processing:expression` should be employed to explicitly define the calculation they represent. +- Removed `norm_clip` property. It is now represented under `scaling` objects with a corresponding `type` definition. +- Removed `norm_by_channel` from `mlm:input` objects. If rescaling (previously normalization in the documentation) + is a single value, broadcasting to the relevant bands should be performed implicitly. + Otherwise, the amount of `scaling` objects should match the number of bands or channels involved in the input. ### Fixed - n/a diff --git a/README.md b/README.md index dce0f89..6d246b9 100644 --- a/README.md +++ b/README.md @@ -255,18 +255,15 @@ set to `true`, there would be no `accelerator` to contain against. To avoid conf ### Model Input Object -| Field Name | Type | Description | -|-------------------------|---------------------------------------------------------|-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| -| name | string | **REQUIRED** Name of the input variable defined by the model. If no explicit name is defined by the model, an informative name (e.g.: `"RGB Time Series"`) can be used instead. | -| bands | \[string \| [Model Band Object](#model-band-object)] | **REQUIRED** The raster band references used to train, fine-tune or perform inference with the model, which may be all or a subset of bands available in a STAC Item's [Band Object](#bands-and-statistics). If no band applies for one input, use an empty array. | -| input | [Input Structure Object](#input-structure-object) | **REQUIRED** The N-dimensional array definition that describes the shape, dimension ordering, and data type. | -| description | string | Additional details about the input such as describing its purpose or expected source that cannot be represented by other properties. | -| norm_by_channel | boolean | Whether to normalize each channel by channel-wise statistics or to normalize by dataset statistics. If True, use an array of `statistics` of same dimensionality and order as the `bands` field in this object. | -| norm_type | [Normalize Enum](#normalize-enum) \| null | Normalization method. Select an appropriate option or `null` when none applies. Consider using `pre_processing_function` for custom implementations or more complex combinations. | -| norm_clip | \[number] | When `norm_type = "clip"`, this array supplies the value for each `bands` item, which is used to divide each band before clipping values between 0 and 1. | -| resize_type | [Resize Enum](#resize-enum) \| null | High-level descriptor of the rescaling method to change image shape. Select an appropriate option or `null` when none applies. Consider using `pre_processing_function` for custom implementations or more complex combinations. | -| statistics | \[[Statistics Object](#bands-and-statistics)] | Dataset statistics for the training dataset used to normalize the inputs. | -| pre_processing_function | [Processing Expression](#processing-expression) \| null | Custom preprocessing function where normalization and rescaling, and any other significant operations takes place. The `pre_processing_function` should be applied over all available `bands`. For respective band operations, see [Model Band Object](#model-band-object). | +| Field Name | Type | Description | +|-------------------------|---------------------------------------------------------|-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| +| name | string | **REQUIRED** Name of the input variable defined by the model. If no explicit name is defined by the model, an informative name (e.g.: `"RGB Time Series"`) can be used instead. | +| bands | \[string \| [Model Band Object](#model-band-object)] | **REQUIRED** The raster band references used to train, fine-tune or perform inference with the model, which may be all or a subset of bands available in a STAC Item's [Band Object](#bands-and-statistics). If no band applies for one input, use an empty array. | +| input | [Input Structure Object](#input-structure-object) | **REQUIRED** The N-dimensional array definition that describes the shape, dimension ordering, and data type. | +| description | string | Additional details about the input such as describing its purpose or expected source that cannot be represented by other properties. | +| scaling | \[[Scaling Object](#bands-and-statistics)] \| null | Method to scale, normalize, or standardize the data inputs, or `null` if none applies. These values often correspond to dataset or sensor statistics employed for training the model, but can come from another source as needed by the model definition. Consider using `pre_processing_function` for custom implementations or more complex combinations. | +| resize_type | [Resize Enum](#resize-enum) \| null | High-level descriptor of the rescaling method to change image shape. Select an appropriate option or `null` when none applies. Consider using `pre_processing_function` for custom implementations or more complex combinations. | +| pre_processing_function | [Processing Expression](#processing-expression) \| null | Custom preprocessing function where normalization and rescaling, and any other significant operations takes place. The `pre_processing_function` should be applied over all available `bands`. For respective band operations, see [Model Band Object](#model-band-object). | Fields that accept the `null` value can be considered `null` when omitted entirely for parsing purposes. However, setting `null` explicitly when this information is known by the model provider can help users understand @@ -319,13 +316,26 @@ An input's `bands` definition can either be a plain `string` or a [Model Band Ob When a `string` is employed directly, the value should be implicitly mapped to the `name` property of the explicit object representation. -One distinction from the [STAC 1.1 - Band Object][stac-1.1-band] in MLM is that [Statistics][stac-1.1-stats] object +One distinction from the [STAC 1.1 - Band Object][stac-1.1-band] in MLM is that [Band Statistics][stac-1.1-stats] object (or the corresponding [STAC Raster - Statistics][stac-raster-stats] for STAC 1.0) are not defined at the "Band Object" level, but at the [Model Input](#model-input-object) level. This is because, in machine learning, it is common to need overall statistics for the dataset used to train the model to normalize all bands, rather than normalizing the values over a single product. Furthermore, statistics could be applied differently for distinct [Model Input](#model-input-object) definitions, in order to adjust for intrinsic -properties of the model. +properties of the model. + +Another distinction is that, depending on the model, statistics could apply to some inputs that have no reference to +any `bands` definition. In such case, defining statistics under `bands` would not be possible, or would intrude +ambiguous definitions. + +Finally, contrary to the "`statistics`" property name employed by [Band Statistics][stac-1.1-stats], MLM employs the +distinct name `scaling`, although similar `minimum`, `maximum`, etc. sub-fields are employed. +This is done explicitly to disambiguate "informative" band statistics from "applied scaling operations" required +by the model inputs. This highlights the fact that `scaling` are not *necessarily* equal +to [Band Statistics][stac-1.1-stats] values, although they are often equal in practice due to the applicable +value-range domains they represent. Also, this allows addressing special scaling cases, using additional properties +unavailable from [Band Statistics][stac-1.1-stats], such as `value`-specific scaling +(see [Scaling Object](#scaling-object) for more details). [stac-1.1-band]: https://github.com/radiantearth/stac-spec/blob/v1.1.0/commons/common-metadata.md#bands [stac-1.1-stats]: https://github.com/radiantearth/stac-spec/blob/v1.1.0/commons/common-metadata.md#statistics-object @@ -406,32 +416,55 @@ Below are some notable common names recommended for use, but others can be emplo For example, a tensor of multiple RBG images represented as $B \times C \times H \times W$ should indicate `dim_order = ["batch", "channel", "height", "width"]`. -#### Normalize Enum +#### Scaling Object Select one option from: -- `min-max` -- `z-score` -- `l1` -- `l2` -- `l2sqr` -- `hamming` -- `hamming2` -- `type-mask` -- `relative` -- `inf` -- `clip` - -See [OpenCV - Normalization Flags][opencv-normalization-flags] -for details about the relevant methods. Equivalent methods from other packages are applicable as well. -When a normalization technique is specified, it is expected that the corresponding [Statistics](#bands-and-statistics) -parameters necessary to perform it would be provided for the corresponding input. -For example, the `min-max` normalization would require that at least the `minimum` and `maximum` statistic properties -are provided, while the `z-score` would require `mean` and `stddev`. +| `type`
([Scaling Enum](#scaling-enum)) | Required Properties | Scaling Operation | +|-------------------------------------------|-------------------------------------------------|------------------------------------------| +| `min-max` | `minimum`, `maximum` | $(data - minimum) / (maximum - minimum)$ | +| `z-score` | `mean`, `stddev` | $(data - mean) / stddev$ | +| `clip` | `minimum`, `maximum` | $\min(\max(data, minimum), maximum)$ | +| `clip-min` | `minimum` | $\max(data, minimum)$ | +| `clip-max` | `maximum` | $\min(data, maximum)$ | +| `offset` | `value` | $data - value$ | +| `scale` | `value` | $data / value$ | +| `processing` | [Processing Expression](#processing-expression) | *according to `processing:expression`* | + +When a scaling `type` approach is specified, it is expected that the parameters necessary +to perform their calculation are provided for the corresponding input dimension data. + +If none of the above values applies for a given dimension, `type: null` (literal `null`, not string) should be +used instead. If none of the input dimension require scaling, the entire definition can be defined as `scaling: null` +or be omitted entirely. + +When `scaling` is specified, the amount of objects defined in the array must match the size of +the bands/channels/dimensions described by the [Model Input](#model-input-object). However, the `scaling` array +is allowed to contain a single object if the entire input must be rescaled using the same definition across all +dimensions. In such case, implicit broadcasting of the unique [Scaling Object](#scaling-object) should be +performed for all applicable dimensions when running inference with the model. + +If a custom scaling operation, or a combination of more complex operations (with or without [Resize](#resize-enum)), +must be defined instead, a [Processing Expression](#processing-expression) reference can be specified in place of +the [Scaling Object](#scaling-object) of the respective input dimension, as shown below. -If none of the above values applies, `null` (literal, not string) can be used instead. -If a custom normalization operation, or a combination of operations (with or without [Resize](#resize-enum)), -must be defined instead, consider using a [Processing Expression](#processing-expression) reference. +```json +{ + "scaling": [ + {"type": "min-max", "minimum": 0, "maximum": 255}, + {"type": "clip", "minimum": 0, "maximum": 255}, + {"type": "processing", "format": "gdal-calc", "expression": "A * logical_or( A<=177, A>=185 )"} + ] +} +``` + +For operations such as L1 or L2 normalization, a [Processing Expression](#processing-expression) should also be employed. +This is because, depending on the [Model Input](#model-input-object) dimensions and reference data, there is an +ambiguity regarding "how" and "where" such normalization functions must be applied against the input data. +A custom mathematical expression should provide explicitly the data manipulation and normalization strategy. + +In situations of very complex `scaling` operations, which cannot be represented by any of the previous definition, +a `pre_processing_function` should be used instead (see [Model Input Object](#model-input-object)). [opencv-normalization-flags]: https://docs.opencv.org/4.x/d2/de8/group__core__array.html#gad12cefbcb5291cf958a85b4b67b6149f @@ -473,7 +506,7 @@ On top of the examples already provided by [Processing Extension - Expression Ob the following formats are recommended as alternative scripts and function references. | Format | Type | Description | Expression Example | -|----------| ------ |----------------------------------------|------------------------------------------------------------------------------------------------------| +|----------|--------|----------------------------------------|------------------------------------------------------------------------------------------------------| | `python` | string | A Python entry point reference. | `my_package.my_module:my_processing_function` or `my_package.my_module:MyClass.my_method` | | `docker` | string | An URI with image and tag to a Docker. | `ghcr.io/NAMESPACE/IMAGE_NAME:latest` | | `uri` | string | An URI to some binary or script. | `{"href": "https://raw.githubusercontent.com/ORG/REPO/TAG/package/cli.py", "type": "text/x-python"}` | diff --git a/json-schema/schema.json b/json-schema/schema.json index 1ca916d..30e18ab 100644 --- a/json-schema/schema.json +++ b/json-schema/schema.json @@ -523,14 +523,8 @@ "type": "string", "minLength": 1 }, - "norm_by_channel": { - "type": "boolean" - }, - "norm_type": { - "$ref": "#/$defs/NormalizeType" - }, - "norm_clip": { - "$ref": "#/$defs/NormalizeClip" + "scaling": { + "$ref": "#/$defs/Scaling" }, "resize_type": { "$ref": "#/$defs/ResizeType" @@ -734,34 +728,164 @@ ] } }, - "NormalizeType": { + "Scaling": { "oneOf": [ { - "type": "string", - "enum": [ - "min-max", - "z-score", - "l1", - "l2", - "l2sqr", - "hamming", - "hamming2", - "type-mask", - "relative", - "inf" - ] + "type": "null" }, { - "type": "null" + "type": "array", + "minItems": 1, + "items": { + "$ref": "#/$defs/ScalingObject" + } } ] }, - "NormalizeClip": { - "type": "array", - "minItems": 1, - "items": { - "type": "number" - } + "ScalingObject": { + "oneOf": [ + { + "type": "object", + "required": [ + "type", + "minimum", + "maximum" + ], + "properties": { + "type": { + "const": "min-max" + }, + "minimum": { + "type": "number" + }, + "maximum": { + "type": "number" + } + } + }, + { + "type": "object", + "required": [ + "type", + "mean", + "stddev" + ], + "properties": { + "type": { + "const": "z-score" + }, + "mean": { + "type": "number" + }, + "stddev": { + "type": "number" + } + } + }, + { + "type": "object", + "required": [ + "type", + "minimum", + "maximum" + ], + "properties": { + "type": { + "const": "clip" + }, + "minimum": { + "type": "number" + }, + "maximum": { + "type": "number" + } + } + }, + { + "type": "object", + "required": [ + "type", + "minimum" + ], + "properties": { + "type": { + "const": "clip-min" + }, + "minimum": { + "type": "number" + }, + "maximum": { + "type": "number" + } + } + }, + { + "type": "object", + "required": [ + "type", + "maximum" + ], + "properties": { + "type": { + "const": "clip-max" + }, + "maximum": { + "type": "number" + } + } + }, + { + "type": "object", + "required": [ + "type", + "value" + ], + "properties": { + "type": { + "const": "offset" + }, + "value": { + "type": "number" + } + } + }, + { + "type": "object", + "required": [ + "type", + "value" + ], + "properties": { + "type": { + "const": "scale" + }, + "value": { + "type": "number" + } + } + }, + { + "$ref": "#/$defs/ScalingProcessingExpression" + } + ] + }, + "ScalingProcessingExpression": { + "allOf": [ + { + "type": "object", + "required": [ + "type" + ], + "properties": { + "type": { + "const": "processing" + } + } + }, + { + "$ref": "#/$defs/ProcessingExpression" + } + ] }, "ResizeType": { "oneOf": [ From a65be790c204fd5ce96a1820894c845d38e778ba Mon Sep 17 00:00:00 2001 From: Francis Charette Migneault Date: Thu, 31 Oct 2024 17:22:14 -0400 Subject: [PATCH 13/83] update tests to match schema --- tests/test_schema.py | 28 ++++++++++++++++------------ 1 file changed, 16 insertions(+), 12 deletions(-) diff --git a/tests/test_schema.py b/tests/test_schema.py index b76e83c..40a7baf 100644 --- a/tests/test_schema.py +++ b/tests/test_schema.py @@ -91,30 +91,34 @@ def test_mlm_no_input_allowed_but_explicit_empty_array_required( indirect=True, ) @pytest.mark.parametrize( - ["test_norm_type", "test_statistics", "is_valid"], + ["test_scaling", "is_valid"], [ - ("min-max", [{"mean": 1, "stddev": 2}], False), - ("z-score", [{"minimum": 1, "maximum": 2}], False), - ("min-max", [{"mean": 1, "stddev": 2}, {"minimum": 1, "maximum": 2}], False), - ("z-score", [{"mean": 1, "stddev": 2}, {"minimum": 1, "maximum": 2}], False), - ("min-max", [{"minimum": 1, "maximum": 2}], True), - ("z-score", [{"mean": 1, "stddev": 2, "minimum": 1, "maximum": 2}], True), # extra statistics must be ignored - ("l2", [{"mean": 1, "stddev": 2}], True), # statistics "don't" care for this norm-type + ([{"type": "unknown", "mean": 1, "stddev": 2}], False), + ([{"type": "min-max", "mean": 1, "stddev": 2}], False), + ([{"type": "z-score", "minimum": 1, "maximum": 2}], False), + ([{"type": "min-max", "mean": 1, "stddev": 2}, {"type": "min-max", "minimum": 1, "maximum": 2}], False), + ([{"type": "z-score", "mean": 1, "stddev": 2}, {"type": "z-score", "minimum": 1, "maximum": 2}], False), + ([{"type": "min-max", "minimum": 1, "maximum": 2}], True), + ([{"type": "z-score", "mean": 1, "stddev": 2, "minimum": 1, "maximum": 2}], True), # extra must be ignored + ([{"type": "processing"}], False), + ([{"type": "processing", "format": "test", "expression": "test"}], True), + ([ + {"type": "processing", "format": "test", "expression": "test"}, + {"type": "min-max", "minimum": 1, "maximum": 2} + ], True), ], ) def test_mlm_input_norm_type_statistics_combination( mlm_validator: STACValidator, mlm_example: Dict[str, JSON], - test_norm_type: str, - test_statistics: List[Dict[str, Any]], + test_scaling: List[Dict[str, Any]], is_valid: bool, ) -> None: mlm_data = copy.deepcopy(mlm_example) mlm_item = pystac.Item.from_dict(mlm_data) pystac.validation.validate(mlm_item, validator=mlm_validator) # ensure original is valid - mlm_data["properties"]["mlm:input"][0]["norm_type"] = test_norm_type - mlm_data["properties"]["mlm:input"][0]["statistics"] = test_statistics + mlm_data["properties"]["mlm:input"][0]["scaling"] = test_scaling mlm_item = pystac.Item.from_dict(mlm_data) if is_valid: pystac.validation.validate(mlm_item, validator=mlm_validator) From 0c73c3970dfe0fdbb0bb42586aa3e920e87fe0a8 Mon Sep 17 00:00:00 2001 From: Francis Charette Migneault Date: Thu, 31 Oct 2024 17:33:05 -0400 Subject: [PATCH 14/83] update examples, schema and tests with scaling definition --- examples/item_eo_and_raster_bands.json | 17 ++++- examples/item_eo_bands.json | 16 ++++- examples/item_eo_bands_summarized.json | 17 ++++- examples/item_multi_io.json | 3 +- examples/item_raster_bands.json | 2 +- json-schema/schema.json | 99 +------------------------- tests/test_schema.py | 2 +- 7 files changed, 46 insertions(+), 110 deletions(-) diff --git a/examples/item_eo_and_raster_bands.json b/examples/item_eo_and_raster_bands.json index d3fe6bf..ab521f1 100644 --- a/examples/item_eo_and_raster_bands.json +++ b/examples/item_eo_and_raster_bands.json @@ -96,59 +96,70 @@ ], "data_type": "float32" }, - "norm_by_channel": true, - "norm_type": "z-score", "resize_type": null, - "statistics": [ + "scaling": [ { + "type": "z-score", "mean": 1354.40546513, "stddev": 245.71762908 }, { + "type": "z-score", "mean": 1118.24399958, "stddev": 333.00778264 }, { + "type": "z-score", "mean": 1042.92983953, "stddev": 395.09249139 }, { + "type": "z-score", "mean": 947.62620298, "stddev": 593.75055589 }, { + "type": "z-score", "mean": 1199.47283961, "stddev": 566.4170017 }, { + "type": "z-score", "mean": 1999.79090914, "stddev": 861.18399006 }, { + "type": "z-score", "mean": 2369.22292565, "stddev": 1086.63139075 }, { + "type": "z-score", "mean": 2296.82608323, "stddev": 1117.98170791 }, { + "type": "z-score", "mean": 732.08340178, "stddev": 404.91978886 }, { + "type": "z-score", "mean": 12.11327804, "stddev": 4.77584468 }, { + "type": "z-score", "mean": 1819.01027855, "stddev": 1002.58768311 }, { + "type": "z-score", "mean": 1118.92391149, "stddev": 761.30323499 }, { + "type": "z-score", "mean": 2594.14080798, "stddev": 1231.58581042 } diff --git a/examples/item_eo_bands.json b/examples/item_eo_bands.json index 23656a1..cd820c8 100644 --- a/examples/item_eo_bands.json +++ b/examples/item_eo_bands.json @@ -98,58 +98,70 @@ "data_type": "float32" }, "norm_by_channel": true, - "norm_type": "z-score", "resize_type": null, - "statistics": [ + "scaling": [ { + "type": "z-score", "mean": 1354.40546513, "stddev": 245.71762908 }, { + "type": "z-score", "mean": 1118.24399958, "stddev": 333.00778264 }, { + "type": "z-score", "mean": 1042.92983953, "stddev": 395.09249139 }, { + "type": "z-score", "mean": 947.62620298, "stddev": 593.75055589 }, { + "type": "z-score", "mean": 1199.47283961, "stddev": 566.4170017 }, { + "type": "z-score", "mean": 1999.79090914, "stddev": 861.18399006 }, { + "type": "z-score", "mean": 2369.22292565, "stddev": 1086.63139075 }, { + "type": "z-score", "mean": 2296.82608323, "stddev": 1117.98170791 }, { + "type": "z-score", "mean": 732.08340178, "stddev": 404.91978886 }, { + "type": "z-score", "mean": 12.11327804, "stddev": 4.77584468 }, { + "type": "z-score", "mean": 1819.01027855, "stddev": 1002.58768311 }, { + "type": "z-score", "mean": 1118.92391149, "stddev": 761.30323499 }, { + "type": "z-score", "mean": 2594.14080798, "stddev": 1231.58581042 } diff --git a/examples/item_eo_bands_summarized.json b/examples/item_eo_bands_summarized.json index 8e20a17..d27cade 100644 --- a/examples/item_eo_bands_summarized.json +++ b/examples/item_eo_bands_summarized.json @@ -97,59 +97,70 @@ ], "data_type": "float32" }, - "norm_by_channel": true, - "norm_type": "z-score", "resize_type": null, - "statistics": [ + "scaling": [ { + "type": "z-score", "mean": 1354.40546513, "stddev": 245.71762908 }, { + "type": "z-score", "mean": 1118.24399958, "stddev": 333.00778264 }, { + "type": "z-score", "mean": 1042.92983953, "stddev": 395.09249139 }, { + "type": "z-score", "mean": 947.62620298, "stddev": 593.75055589 }, { + "type": "z-score", "mean": 1199.47283961, "stddev": 566.4170017 }, { + "type": "z-score", "mean": 1999.79090914, "stddev": 861.18399006 }, { + "type": "z-score", "mean": 2369.22292565, "stddev": 1086.63139075 }, { + "type": "z-score", "mean": 2296.82608323, "stddev": 1117.98170791 }, { + "type": "z-score", "mean": 732.08340178, "stddev": 404.91978886 }, { + "type": "z-score", "mean": 12.11327804, "stddev": 4.77584468 }, { + "type": "z-score", "mean": 1819.01027855, "stddev": 1002.58768311 }, { + "type": "z-score", "mean": 1118.92391149, "stddev": 761.30323499 }, { + "type": "z-score", "mean": 2594.14080798, "stddev": 1231.58581042 } diff --git a/examples/item_multi_io.json b/examples/item_multi_io.json index 14c63f9..e92fe6d 100644 --- a/examples/item_multi_io.json +++ b/examples/item_multi_io.json @@ -85,8 +85,7 @@ ], "data_type": "uint16" }, - "norm_by_channel": false, - "norm_type": null, + "scaling": null, "resize_type": null }, { diff --git a/examples/item_raster_bands.json b/examples/item_raster_bands.json index b97b06e..24b36a5 100644 --- a/examples/item_raster_bands.json +++ b/examples/item_raster_bands.json @@ -95,7 +95,7 @@ ], "data_type": "float32" }, - "norm_type": null, + "scaling": null, "resize_type": null, "pre_processing_function": { "format": "python", diff --git a/json-schema/schema.json b/json-schema/schema.json index 30e18ab..76532d9 100644 --- a/json-schema/schema.json +++ b/json-schema/schema.json @@ -490,14 +490,7 @@ "mlm:input": { "type": "array", "items": { - "allOf": [ - { - "$ref": "#/$defs/ModelInput" - }, - { - "$ref": "#/$defs/ModelInputNormTypeStatistics" - } - ] + "$ref": "#/$defs/ModelInput" } }, "ModelInput": { @@ -529,93 +522,11 @@ "resize_type": { "$ref": "#/$defs/ResizeType" }, - "statistics": { - "$ref": "#/$defs/InputStatistics" - }, "pre_processing_function": { "$ref": "#/$defs/ProcessingExpression" } } }, - "ModelInputNormTypeStatistics": { - "description": "Validates the corresponding statistics required for the specified normalization type.", - "allOf": [ - { - "if": { - "type": "object", - "required": [ - "norm_type" - ], - "properties": { - "norm_type": { - "const": "min-max" - } - } - }, - "then": { - "type": "object", - "required": [ - "statistics" - ], - "properties": { - "statistics": { - "allOf": [ - { - "$ref": "#/$defs/InputStatistics" - }, - { - "type": "array", - "items": { - "required": [ - "minimum", - "maximum" - ] - } - } - ] - } - } - } - }, - { - "if": { - "type": "object", - "required": [ - "norm_type" - ], - "properties": { - "norm_type": { - "const": "z-score" - } - } - }, - "then": { - "type": "object", - "required": [ - "statistics" - ], - "properties": { - "statistics": { - "allOf": [ - { - "$ref": "#/$defs/InputStatistics" - }, - { - "type": "array", - "items": { - "required": [ - "mean", - "stddev" - ] - } - } - ] - } - } - } - } - ] - }, "mlm:output": { "type": "array", "items": { @@ -921,14 +832,6 @@ } ] }, - "InputStatistics": { - "$comment": "MLM statistics for the specific input relevant for normalization for ML features.", - "type": "array", - "minItems": 1, - "items": { - "$ref": "https://stac-extensions.github.io/raster/v1.1.0/schema.json#/definitions/bands/items/properties/statistics" - } - }, "ProcessingExpression": { "oneOf": [ { diff --git a/tests/test_schema.py b/tests/test_schema.py index 40a7baf..4f29bbf 100644 --- a/tests/test_schema.py +++ b/tests/test_schema.py @@ -108,7 +108,7 @@ def test_mlm_no_input_allowed_but_explicit_empty_array_required( ], True), ], ) -def test_mlm_input_norm_type_statistics_combination( +def test_mlm_input_scaling_combination( mlm_validator: STACValidator, mlm_example: Dict[str, JSON], test_scaling: List[Dict[str, Any]], From 984ee7744019bd38225d5ead779ef58ed9ec099b Mon Sep 17 00:00:00 2001 From: Francis Charette Migneault Date: Thu, 31 Oct 2024 23:47:11 -0400 Subject: [PATCH 15/83] fix linting --- README.md | 26 ++++++++++++-------------- 1 file changed, 12 insertions(+), 14 deletions(-) diff --git a/README.md b/README.md index 6d246b9..f58ce2d 100644 --- a/README.md +++ b/README.md @@ -420,16 +420,16 @@ indicate `dim_order = ["batch", "channel", "height", "width"]`. Select one option from: -| `type`
([Scaling Enum](#scaling-enum)) | Required Properties | Scaling Operation | -|-------------------------------------------|-------------------------------------------------|------------------------------------------| -| `min-max` | `minimum`, `maximum` | $(data - minimum) / (maximum - minimum)$ | -| `z-score` | `mean`, `stddev` | $(data - mean) / stddev$ | -| `clip` | `minimum`, `maximum` | $\min(\max(data, minimum), maximum)$ | -| `clip-min` | `minimum` | $\max(data, minimum)$ | -| `clip-max` | `maximum` | $\min(data, maximum)$ | -| `offset` | `value` | $data - value$ | -| `scale` | `value` | $data / value$ | -| `processing` | [Processing Expression](#processing-expression) | *according to `processing:expression`* | +| `type` | Required Properties | Scaling Operation | +|--------------|-------------------------------------------------|------------------------------------------| +| `min-max` | `minimum`, `maximum` | $(data - minimum) / (maximum - minimum)$ | +| `z-score` | `mean`, `stddev` | $(data - mean) / stddev$ | +| `clip` | `minimum`, `maximum` | $\min(\max(data, minimum), maximum)$ | +| `clip-min` | `minimum` | $\max(data, minimum)$ | +| `clip-max` | `maximum` | $\min(data, maximum)$ | +| `offset` | `value` | $data - value$ | +| `scale` | `value` | $data / value$ | +| `processing` | [Processing Expression](#processing-expression) | *according to `processing:expression`* | When a scaling `type` approach is specified, it is expected that the parameters necessary to perform their calculation are provided for the corresponding input dimension data. @@ -458,7 +458,7 @@ the [Scaling Object](#scaling-object) of the respective input dimension, as show } ``` -For operations such as L1 or L2 normalization, a [Processing Expression](#processing-expression) should also be employed. +For operations such as L1 or L2 normalization, [Processing Expression](#processing-expression) should also be employed. This is because, depending on the [Model Input](#model-input-object) dimensions and reference data, there is an ambiguity regarding "how" and "where" such normalization functions must be applied against the input data. A custom mathematical expression should provide explicitly the data manipulation and normalization strategy. @@ -466,8 +466,6 @@ A custom mathematical expression should provide explicitly the data manipulation In situations of very complex `scaling` operations, which cannot be represented by any of the previous definition, a `pre_processing_function` should be used instead (see [Model Input Object](#model-input-object)). -[opencv-normalization-flags]: https://docs.opencv.org/4.x/d2/de8/group__core__array.html#gad12cefbcb5291cf958a85b4b67b6149f - #### Resize Enum Select one option from: @@ -486,7 +484,7 @@ See [OpenCV - Interpolation Flags][opencv-interpolation-flags] for details about the relevant methods. Equivalent methods from other packages are applicable as well. If none of the above values applies, `null` (literal, not string) can be used instead. -If a custom rescaling operation, or a combination of operations (with or without [Normalization](#normalize-enum)), +If a custom rescaling operation, or a combination of operations (with or without [Scaling](#scaling-object)), must be defined instead, consider using a [Processing Expression](#processing-expression) reference. [opencv-interpolation-flags]: https://docs.opencv.org/4.x/da/d54/group__imgproc__transform.html#ga5bb5a1fea74ea38e1a5445ca803ff121 From 473cee7ee70336ba7924f89d00f7a1c3894e4d94 Mon Sep 17 00:00:00 2001 From: Francis Charette Migneault Date: Fri, 1 Nov 2024 00:08:28 -0400 Subject: [PATCH 16/83] update stac-model with corresponding scaling definitions from JSON schema --- stac_model/examples.py | 18 +++++----- stac_model/input.py | 76 ++++++++++++++++++++++++++++-------------- tests/test_schema.py | 2 +- 3 files changed, 62 insertions(+), 34 deletions(-) diff --git a/stac_model/examples.py b/stac_model/examples.py index d882623..573a6b2 100644 --- a/stac_model/examples.py +++ b/stac_model/examples.py @@ -7,7 +7,7 @@ from pystac.extensions.file import FileExtension from stac_model.base import ProcessingExpression -from stac_model.input import InputStructure, MLMStatistic, ModelInput +from stac_model.input import InputStructure, ScalingObject, ModelInput from stac_model.output import MLMClassification, ModelOutput, ModelResult from stac_model.schema import ItemMLModelExtension, MLModelExtension, MLModelProperties @@ -63,10 +63,14 @@ def eurosat_resnet() -> ItemMLModelExtension: 761.30323499, 1231.58581042, ] - stats = [ - MLMStatistic( - mean=mean, - stddev=stddev, + scaling = [ + cast( + ScalingObject, + dict( + type="z-score", + mean=mean, + stddev=stddev, + ) ) for mean, stddev in zip(stats_mean, stats_stddev) ] @@ -74,10 +78,8 @@ def eurosat_resnet() -> ItemMLModelExtension: name="13 Band Sentinel-2 Batch", bands=band_names, input=input_struct, - norm_by_channel=True, - norm_type="z-score", resize_type=None, - statistics=stats, + scaling=scaling, pre_processing_function=ProcessingExpression( format="python", expression="torchgeo.datamodules.eurosat.EuroSATDataModule.collate_fn", diff --git a/stac_model/input.py b/stac_model/input.py index 22788d7..aa41d1c 100644 --- a/stac_model/input.py +++ b/stac_model/input.py @@ -7,8 +7,8 @@ class InputStructure(MLMBaseModel): - shape: List[Union[int, float]] = Field(min_items=1) - dim_order: List[str] = Field(min_items=1) + shape: List[Union[int, float]] = Field(min_length=1) + dim_order: List[str] = Field(min_length=1) data_type: DataType @model_validator(mode="after") @@ -18,27 +18,56 @@ def validate_dimensions(self) -> Self: return self -class MLMStatistic(MLMBaseModel): # FIXME: add 'Statistics' dep from raster extension (cases required to be triggered) - minimum: Annotated[Optional[Number], OmitIfNone] = None - maximum: Annotated[Optional[Number], OmitIfNone] = None - mean: Annotated[Optional[Number], OmitIfNone] = None - stddev: Annotated[Optional[Number], OmitIfNone] = None - count: Annotated[Optional[int], OmitIfNone] = None - valid_percent: Annotated[Optional[Number], OmitIfNone] = None +class ScalingClipMin(MLMBaseModel): + type: Literal["clip-min"] = "clip-min" + minimum: Number -NormalizeType: TypeAlias = Optional[ - Literal[ - "min-max", - "z-score", - "l1", - "l2", - "l2sqr", - "hamming", - "hamming2", - "type-mask", - "relative", - "inf", +class ScalingClipMax(MLMBaseModel): + type: Literal["clip-max"] = "clip-max" + maximum: Number + + +class ScalingClip(ScalingClipMin, ScalingClipMax): + type: Literal["clip"] = "clip" + + +class ScalingMinMax(MLMBaseModel): + type: Literal["min-max"] = "min-max" + minimum: Number + maximum: Number + + +class ScalingZScore(MLMBaseModel): + type: Literal["z-score"] = "z-score" + mean: Number + stddev: Number + + +class ScalingOffset(MLMBaseModel): + type: Literal["offset"] = "offset" + value: Number + + +class ScalingScale(MLMBaseModel): + type: Literal["scale"] = "scale" + value: Number + + +class ScalingProcessingExpression(ProcessingExpression): + type: Literal["processing"] = "processing" + + +ScalingObject: TypeAlias = Optional[ + Union[ + ScalingMinMax, + ScalingZScore, + ScalingClip, + ScalingClipMin, + ScalingClipMax, + ScalingOffset, + ScalingScale, + ScalingProcessingExpression, ] ] @@ -107,9 +136,6 @@ class ModelInput(MLMBaseModel): ], ) input: InputStructure - norm_by_channel: Annotated[Optional[bool], OmitIfNone] = None - norm_type: Annotated[Optional[NormalizeType], OmitIfNone] = None - norm_clip: Annotated[Optional[List[Union[float, int]]], OmitIfNone] = None + scaling: Annotated[Optional[List[ScalingObject]], OmitIfNone] = None resize_type: Annotated[Optional[ResizeType], OmitIfNone] = None - statistics: Annotated[Optional[List[MLMStatistic]], OmitIfNone] = None pre_processing_function: Optional[ProcessingExpression] = None diff --git a/tests/test_schema.py b/tests/test_schema.py index 4f29bbf..73e9789 100644 --- a/tests/test_schema.py +++ b/tests/test_schema.py @@ -118,7 +118,7 @@ def test_mlm_input_scaling_combination( mlm_item = pystac.Item.from_dict(mlm_data) pystac.validation.validate(mlm_item, validator=mlm_validator) # ensure original is valid - mlm_data["properties"]["mlm:input"][0]["scaling"] = test_scaling + mlm_data["properties"]["mlm:input"][0]["scaling"] = test_scaling # type: ignore mlm_item = pystac.Item.from_dict(mlm_data) if is_valid: pystac.validation.validate(mlm_item, validator=mlm_validator) From 323fb8f4b00f53a66d99d46ea5e0bc2974922d1c Mon Sep 17 00:00:00 2001 From: Francis Charette Migneault Date: Fri, 1 Nov 2024 00:10:17 -0400 Subject: [PATCH 17/83] fix import linting --- stac_model/examples.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/stac_model/examples.py b/stac_model/examples.py index 573a6b2..497ae93 100644 --- a/stac_model/examples.py +++ b/stac_model/examples.py @@ -7,7 +7,7 @@ from pystac.extensions.file import FileExtension from stac_model.base import ProcessingExpression -from stac_model.input import InputStructure, ScalingObject, ModelInput +from stac_model.input import InputStructure, ModelInput, ScalingObject from stac_model.output import MLMClassification, ModelOutput, ModelResult from stac_model.schema import ItemMLModelExtension, MLModelExtension, MLModelProperties From d36a3740ee23188181ad089c93f811302b6c63c1 Mon Sep 17 00:00:00 2001 From: Francis Charette Migneault Date: Fri, 1 Nov 2024 00:12:52 -0400 Subject: [PATCH 18/83] avoid typing error due to literal type override --- stac_model/input.py | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/stac_model/input.py b/stac_model/input.py index aa41d1c..20dc84e 100644 --- a/stac_model/input.py +++ b/stac_model/input.py @@ -28,8 +28,10 @@ class ScalingClipMax(MLMBaseModel): maximum: Number -class ScalingClip(ScalingClipMin, ScalingClipMax): +class ScalingClip(MLMBaseModel): type: Literal["clip"] = "clip" + minimum: Number + maximum: Number class ScalingMinMax(MLMBaseModel): From d1b9409adca4fda6a6434ae7cdef11e6bbc7d090 Mon Sep 17 00:00:00 2001 From: Francis Charette Migneault Date: Fri, 1 Nov 2024 00:21:19 -0400 Subject: [PATCH 19/83] fix disallowing undefined mlm-prefixed fields (fixes #41) --- CHANGELOG.md | 3 ++- json-schema/schema.json | 2 +- tests/test_schema.py | 23 +++++++++++++++++++++++ 3 files changed, 26 insertions(+), 2 deletions(-) diff --git a/CHANGELOG.md b/CHANGELOG.md index 116e1d3..9a47ba8 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -20,7 +20,8 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0 - n/a ### Fixed -- n/a +- Fix check of disallowed unknown/undefined `mlm:`-prefixed fields + (fixes [#41](https://github.com/stac-extensions/mlm/issues/41)). ## [v1.3.0](https://github.com/stac-extensions/mlm/tree/v1.3.0) diff --git a/json-schema/schema.json b/json-schema/schema.json index ff21357..2072404 100644 --- a/json-schema/schema.json +++ b/json-schema/schema.json @@ -314,7 +314,7 @@ }, "$comment": "Allow properties not defined by MLM prefix to allow combination with other extensions.", "patternProperties": { - "^(?!dlm:)": {} + "^(?!mlm:)": {} }, "additionalProperties": false }, diff --git a/tests/test_schema.py b/tests/test_schema.py index 4755978..3958b55 100644 --- a/tests/test_schema.py +++ b/tests/test_schema.py @@ -27,6 +27,29 @@ def test_mlm_schema( assert SCHEMA_URI in validated +@pytest.mark.parametrize( + "mlm_example", + ["item_raster_bands.json"], + indirect=True, +) +def test_mlm_no_undefined_prefixed_field( + mlm_validator: STACValidator, + mlm_example: Dict[str, JSON], +) -> None: + mlm_data = copy.deepcopy(mlm_example) + mlm_item = pystac.Item.from_dict(mlm_data) + pystac.validation.validate(mlm_item, validator=mlm_validator) # ensure original is valid + + mlm_data["properties"]["mlm:unknown"] = "random" + with pytest.raises(pystac.errors.STACValidationError) as exc: + mlm_item = pystac.Item.from_dict(mlm_data) + pystac.validation.validate(mlm_item, validator=mlm_validator) + assert all( + field in str(exc.value.source) + for field in ["mlm:unknown", "^(?!mlm:)"] + ) + + @pytest.mark.parametrize( "mlm_example", ["item_raster_bands.json"], From 1ae124229ee4f4577daa37bdaf5def4edd7a8e30 Mon Sep 17 00:00:00 2001 From: Ryan Avery Date: Fri, 1 Nov 2024 10:00:33 -0400 Subject: [PATCH 20/83] check npm versions in CI, reset before npm run format-markdown --- .github/workflows/stac-model.yml | 1 + 1 file changed, 1 insertion(+) diff --git a/.github/workflows/stac-model.yml b/.github/workflows/stac-model.yml index 1881d7f..4fb75b8 100644 --- a/.github/workflows/stac-model.yml +++ b/.github/workflows/stac-model.yml @@ -30,6 +30,7 @@ jobs: - name: Run checks run: | make lint-all + npm list - name: Run tests run: | From 634bf2b81161ece7c3bb3e43fd01a1e4d68ca8d3 Mon Sep 17 00:00:00 2001 From: Ryan Avery Date: Fri, 1 Nov 2024 10:19:54 -0400 Subject: [PATCH 21/83] gitignore reqs generated from make --- .github/workflows/stac-model.yml | 1 - .github/workflows/test.yaml | 1 + .gitignore | 2 ++ Makefile | 2 +- 4 files changed, 4 insertions(+), 2 deletions(-) diff --git a/.github/workflows/stac-model.yml b/.github/workflows/stac-model.yml index 4fb75b8..1881d7f 100644 --- a/.github/workflows/stac-model.yml +++ b/.github/workflows/stac-model.yml @@ -30,7 +30,6 @@ jobs: - name: Run checks run: | make lint-all - npm list - name: Run tests run: | diff --git a/.github/workflows/test.yaml b/.github/workflows/test.yaml index 2260534..03169a5 100644 --- a/.github/workflows/test.yaml +++ b/.github/workflows/test.yaml @@ -12,3 +12,4 @@ jobs: - run: | npm install npm test + npm list diff --git a/.gitignore b/.gitignore index 4d99463..e82a7d8 100644 --- a/.gitignore +++ b/.gitignore @@ -1,3 +1,5 @@ +requirements*.txt + ### ArchLinuxPackages ### *.tar *.tar.* diff --git a/Makefile b/Makefile index f890680..b3f4de7 100644 --- a/Makefile +++ b/Makefile @@ -87,7 +87,7 @@ lint: setup .PHONY: check-lint check-lint: lint -uv run --python $(ACTIVEPYTHON) ruff check --config=pyproject.toml ./ + uv run --python $(ACTIVEPYTHON) ruff check --config=pyproject.toml ./ .PHONY: format-lint format-lint: setup lint From 305e40cf2ef29b2a1a79f98298712427d4a9c497 Mon Sep 17 00:00:00 2001 From: Ryan Avery Date: Fri, 1 Nov 2024 10:22:43 -0400 Subject: [PATCH 22/83] poetry>uv in actions --- .github/workflows/publish.yaml | 8 ++++---- .github/workflows/stac-model.yml | 6 +++--- pyproject.toml | 2 +- 3 files changed, 8 insertions(+), 8 deletions(-) diff --git a/.github/workflows/publish.yaml b/.github/workflows/publish.yaml index 8b56119..ff89a22 100644 --- a/.github/workflows/publish.yaml +++ b/.github/workflows/publish.yaml @@ -25,9 +25,9 @@ jobs: uses: actions/setup-python@v5.2.0 with: python-version: "3.10" - - name: Install poetry - run: make poetry-install + - name: Install uv + run: make setup - name: Publish stac-model to PyPI run: | - poetry build - poetry publish --username __token__ --password ${{ secrets.PYPI_SECRET }} + uv build + uv publish --username __token__ --password ${{ secrets.PYPI_SECRET }} diff --git a/.github/workflows/stac-model.yml b/.github/workflows/stac-model.yml index 1881d7f..5d42720 100644 --- a/.github/workflows/stac-model.yml +++ b/.github/workflows/stac-model.yml @@ -16,14 +16,14 @@ jobs: with: python-version: ${{ matrix.python-version }} - - name: Install poetry - run: make poetry-install + - name: Install uv + run: make setup - name: Set up cache uses: actions/cache@v4.0.2 with: path: .venv - key: venv-${{ matrix.python-version }}-${{ hashFiles('pyproject.toml') }}-${{ hashFiles('poetry.lock') }} + key: venv-${{ matrix.python-version }}-${{ hashFiles('pyproject.toml') }}-${{ hashFiles('uv.lock') }} - name: Install dependencies run: make install-dev diff --git a/pyproject.toml b/pyproject.toml index 6bd1c10..7413902 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -86,7 +86,7 @@ stac-model = "stac_model.__main__:app" # NOTE: # Although these definitions are provided in this 'stac-model' project file, # they are actually intented for versioning the MLM specification itself. -# To version 'stac-model', use the 'poetry version' operations. +# To version 'stac-model', use the 'bumpversion bump' operation. # See also https://github.com/stac-extensions/mlm/blob/main/CONTRIBUTING.md#building-and-releasing current_version = "1.3.0" parse = "(?P\\d+)\\.(?P\\d+)\\.(?P\\d+)" From 6094b22b05afb4b7df203a630977dee2e7240e7c Mon Sep 17 00:00:00 2001 From: Ryan Avery Date: Fri, 1 Nov 2024 10:44:37 -0400 Subject: [PATCH 23/83] code linting with ruff, fix makefile command, ignore line length and type annotation rules --- Makefile | 1 - pyproject.toml | 1 + stac_model/base.py | 13 ++------ stac_model/examples.py | 2 +- stac_model/input.py | 67 +++++++++++++----------------------------- stac_model/output.py | 20 ++++++------- stac_model/runtime.py | 18 ++++++------ stac_model/schema.py | 23 +++++++-------- tests/conftest.py | 10 +++---- tests/test_schema.py | 18 ++++++------ 10 files changed, 69 insertions(+), 104 deletions(-) diff --git a/Makefile b/Makefile index b3f4de7..90cc310 100644 --- a/Makefile +++ b/Makefile @@ -77,7 +77,6 @@ check-citation: .PHONY: check-safety check-safety: setup - uv check uv run --python $(ACTIVEPYTHON) safety check --full-report uv run --python $(ACTIVEPYTHON) bandit -ll --recursive stac_model tests diff --git a/pyproject.toml b/pyproject.toml index 7413902..0bd5f97 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -185,6 +185,7 @@ search = "https://stac-extensions.github.io/mlm/v{current_version}/schema.json" replace = "https://stac-extensions.github.io/mlm/v{new_version}/schema.json" [tool.ruff] +ignore = ["UP007", "E501"] exclude = [ ".git", "__pycache__", diff --git a/stac_model/base.py b/stac_model/base.py index c5ff7c6..772cb12 100644 --- a/stac_model/base.py +++ b/stac_model/base.py @@ -1,18 +1,11 @@ from dataclasses import dataclass from enum import Enum -from typing import Any, Dict, List, Literal, TypeAlias, Union +from typing import Any, Literal, TypeAlias, Union from pydantic import BaseModel, ConfigDict, model_serializer -Number: TypeAlias = Union[int, float] -JSON: TypeAlias = Union[ - Dict[str, "JSON"], - List["JSON"], - Number, - bool, - str, - None, -] +Number: TypeAlias = int | float +JSON: TypeAlias = dict[str, "JSON"] | list["JSON"] | Number | bool | str | None @dataclass diff --git a/stac_model/examples.py b/stac_model/examples.py index d882623..dbd0657 100644 --- a/stac_model/examples.py +++ b/stac_model/examples.py @@ -68,7 +68,7 @@ def eurosat_resnet() -> ItemMLModelExtension: mean=mean, stddev=stddev, ) - for mean, stddev in zip(stats_mean, stats_stddev) + for mean, stddev in zip(stats_mean, stats_stddev, strict=False) ] model_input = ModelInput( name="13 Band Sentinel-2 Batch", diff --git a/stac_model/input.py b/stac_model/input.py index 22788d7..3001941 100644 --- a/stac_model/input.py +++ b/stac_model/input.py @@ -1,4 +1,5 @@ -from typing import Annotated, Any, List, Literal, Optional, Sequence, TypeAlias, Union +from collections.abc import Sequence +from typing import Annotated, Any, Literal, TypeAlias from typing_extensions import Self from pydantic import Field, model_validator @@ -7,8 +8,8 @@ class InputStructure(MLMBaseModel): - shape: List[Union[int, float]] = Field(min_items=1) - dim_order: List[str] = Field(min_items=1) + shape: list[int | float] = Field(min_items=1) + dim_order: list[str] = Field(min_items=1) data_type: DataType @model_validator(mode="after") @@ -19,43 +20,17 @@ def validate_dimensions(self) -> Self: class MLMStatistic(MLMBaseModel): # FIXME: add 'Statistics' dep from raster extension (cases required to be triggered) - minimum: Annotated[Optional[Number], OmitIfNone] = None - maximum: Annotated[Optional[Number], OmitIfNone] = None - mean: Annotated[Optional[Number], OmitIfNone] = None - stddev: Annotated[Optional[Number], OmitIfNone] = None - count: Annotated[Optional[int], OmitIfNone] = None - valid_percent: Annotated[Optional[Number], OmitIfNone] = None + minimum: Annotated[Number | None, OmitIfNone] = None + maximum: Annotated[Number | None, OmitIfNone] = None + mean: Annotated[Number | None, OmitIfNone] = None + stddev: Annotated[Number | None, OmitIfNone] = None + count: Annotated[int | None, OmitIfNone] = None + valid_percent: Annotated[Number | None, OmitIfNone] = None -NormalizeType: TypeAlias = Optional[ - Literal[ - "min-max", - "z-score", - "l1", - "l2", - "l2sqr", - "hamming", - "hamming2", - "type-mask", - "relative", - "inf", - ] -] +NormalizeType: TypeAlias = Literal["min-max", "z-score", "l1", "l2", "l2sqr", "hamming", "hamming2", "type-mask", "relative", "inf"] | None -ResizeType: TypeAlias = Optional[ - Literal[ - "crop", - "pad", - "interpolation-nearest", - "interpolation-linear", - "interpolation-cubic", - "interpolation-area", - "interpolation-lanczos4", - "interpolation-max", - "wrap-fill-outliers", - "wrap-inverse-map", - ] -] +ResizeType: TypeAlias = Literal["crop", "pad", "interpolation-nearest", "interpolation-linear", "interpolation-cubic", "interpolation-area", "interpolation-lanczos4", "interpolation-max", "wrap-fill-outliers", "wrap-inverse-map"] | None class ModelBand(MLMBaseModel): @@ -66,11 +41,11 @@ class ModelBand(MLMBaseModel): ) ) # similar to 'ProcessingExpression', but they can be omitted here - format: Annotated[Optional[str], OmitIfNone] = Field( + format: Annotated[str | None, OmitIfNone] = Field( default=None, description="", ) - expression: Annotated[Optional[Any], OmitIfNone] = Field( + expression: Annotated[Any | None, OmitIfNone] = Field( default=None, description="", ) @@ -88,7 +63,7 @@ def validate_expression(self) -> Self: class ModelInput(MLMBaseModel): name: str # order is critical here (same index as dim shape), allow duplicate if the model needs it somehow - bands: Sequence[Union[str, ModelBand]] = Field( + bands: Sequence[str | ModelBand] = Field( description=( "List of bands that compose the input. " "If a string is used, it is implied to correspond to a named-band. " @@ -107,9 +82,9 @@ class ModelInput(MLMBaseModel): ], ) input: InputStructure - norm_by_channel: Annotated[Optional[bool], OmitIfNone] = None - norm_type: Annotated[Optional[NormalizeType], OmitIfNone] = None - norm_clip: Annotated[Optional[List[Union[float, int]]], OmitIfNone] = None - resize_type: Annotated[Optional[ResizeType], OmitIfNone] = None - statistics: Annotated[Optional[List[MLMStatistic]], OmitIfNone] = None - pre_processing_function: Optional[ProcessingExpression] = None + norm_by_channel: Annotated[bool | None, OmitIfNone] = None + norm_type: Annotated[NormalizeType | None, OmitIfNone] = None + norm_clip: Annotated[list[float | int] | None, OmitIfNone] = None + resize_type: Annotated[ResizeType | None, OmitIfNone] = None + statistics: Annotated[list[MLMStatistic] | None, OmitIfNone] = None + pre_processing_function: ProcessingExpression | None = None diff --git a/stac_model/output.py b/stac_model/output.py index 0e25ec7..5b1d2ea 100644 --- a/stac_model/output.py +++ b/stac_model/output.py @@ -1,4 +1,4 @@ -from typing import Annotated, Any, Dict, List, Optional, Set, Union, cast +from typing import Annotated, Any, cast from pydantic import AliasChoices, ConfigDict, Field, model_serializer from pystac.extensions.classification import Classification @@ -7,8 +7,8 @@ class ModelResult(MLMBaseModel): - shape: List[Union[int, float]] = Field(..., min_items=1) - dim_order: List[str] = Field(..., min_items=1) + shape: list[int | float] = Field(..., min_items=1) + dim_order: list[str] = Field(..., min_items=1) data_type: DataType @@ -32,15 +32,15 @@ class ModelResult(MLMBaseModel): class MLMClassification(MLMBaseModel, Classification): @model_serializer() - def model_dump(self, *_: Any, **__: Any) -> Dict[str, Any]: + def model_dump(self, *_: Any, **__: Any) -> dict[str, Any]: return self.to_dict() # type: ignore[call-arg] def __init__( self, value: int, - description: Optional[str] = None, - name: Optional[str] = None, - color_hint: Optional[str] = None, + description: str | None = None, + name: str | None = None, + color_hint: str | None = None, ) -> None: Classification.__init__(self, {}) if not name and not description: @@ -78,7 +78,7 @@ def __setattr__(self, key: str, value: Any) -> None: class ModelOutput(MLMBaseModel): name: str - tasks: Set[ModelTask] + tasks: set[ModelTask] result: ModelResult # NOTE: @@ -86,11 +86,11 @@ class ModelOutput(MLMBaseModel): # it is more important to keep the order in this case, # which we would lose with 'Set'. # We also get some unhashable errors with 'Set', although 'MLMClassification' implements '__hash__'. - classes: Annotated[List[MLMClassification], OmitIfNone] = Field( + classes: Annotated[list[MLMClassification], OmitIfNone] = Field( alias="classification:classes", validation_alias=AliasChoices("classification:classes", "classification_classes", "classes"), ) - post_processing_function: Optional[ProcessingExpression] = None + post_processing_function: ProcessingExpression | None = None model_config = ConfigDict( populate_by_name=True, diff --git a/stac_model/runtime.py b/stac_model/runtime.py index 9104fa6..197f7b8 100644 --- a/stac_model/runtime.py +++ b/stac_model/runtime.py @@ -1,5 +1,5 @@ from enum import Enum -from typing import Annotated, Literal, Optional, Union +from typing import Annotated, Literal, Union from pydantic import AliasChoices, Field @@ -33,17 +33,17 @@ def __str__(self): class Runtime(MLMBaseModel): - framework: Annotated[Optional[str], OmitIfNone] = Field(default=None) - framework_version: Annotated[Optional[str], OmitIfNone] = Field(default=None) - file_size: Annotated[Optional[int], OmitIfNone] = Field( + framework: Annotated[str | None, OmitIfNone] = Field(default=None) + framework_version: Annotated[str | None, OmitIfNone] = Field(default=None) + file_size: Annotated[int | None, OmitIfNone] = Field( alias="file:size", validation_alias=AliasChoices("file_size", "file:size"), default=None, ) - memory_size: Annotated[Optional[int], OmitIfNone] = Field(default=None) - batch_size_suggestion: Annotated[Optional[int], OmitIfNone] = Field(default=None) + memory_size: Annotated[int | None, OmitIfNone] = Field(default=None) + batch_size_suggestion: Annotated[int | None, OmitIfNone] = Field(default=None) - accelerator: Optional[AcceleratorType] = Field(default=None) + accelerator: AcceleratorType | None = Field(default=None) accelerator_constrained: bool = Field(default=False) - accelerator_summary: Annotated[Optional[str], OmitIfNone] = Field(default=None) - accelerator_count: Annotated[Optional[int], OmitIfNone] = Field(default=None, minimum=1) + accelerator_summary: Annotated[str | None, OmitIfNone] = Field(default=None) + accelerator_count: Annotated[int | None, OmitIfNone] = Field(default=None, minimum=1) diff --git a/stac_model/schema.py b/stac_model/schema.py index 63310ba..aea1e41 100644 --- a/stac_model/schema.py +++ b/stac_model/schema.py @@ -1,13 +1,10 @@ import json +from collections.abc import Iterable from typing import ( Annotated, Any, Generic, - Iterable, - List, Literal, - Optional, - Set, TypeVar, Union, cast, @@ -48,13 +45,13 @@ def mlm_prefix_adder(field_name: str) -> str: class MLModelProperties(Runtime): name: str = Field(min_length=1) architecture: str = Field(min_length=1) - tasks: Set[ModelTask] - input: List[ModelInput] - output: List[ModelOutput] + tasks: set[ModelTask] + input: list[ModelInput] + output: list[ModelOutput] total_parameters: int - pretrained: Annotated[Optional[bool], OmitIfNone] = Field(default=True) - pretrained_source: Annotated[Optional[str], OmitIfNone] = None + pretrained: Annotated[bool | None, OmitIfNone] = Field(default=True) + pretrained_source: Annotated[str | None, OmitIfNone] = None model_config = ConfigDict(alias_generator=mlm_prefix_adder, populate_by_name=True, extra="ignore") @@ -80,7 +77,7 @@ def name(self) -> SchemaName: def apply( self, - properties: Union[MLModelProperties, dict[str, Any]], + properties: MLModelProperties | dict[str, Any], ) -> None: """ Applies Machine Learning Model Extension properties to the extended :mod:`~pystac` object. @@ -118,7 +115,7 @@ def ext(cls, obj: pystac.Collection, add_if_missing: bool = False) -> "Collectio @classmethod def ext( cls, - obj: Union[pystac.Collection, pystac.Item, pystac.Asset], # item_assets.AssetDefinition + obj: pystac.Collection | pystac.Item | pystac.Asset, # item_assets.AssetDefinition add_if_missing: bool = False, ) -> Union[ "CollectionMLModelExtension", @@ -186,7 +183,7 @@ def _validate_mlm_property(self, prop: str, summaries: list[Any]) -> None: for value in summaries: validator.validate_assignment(model, prop, value) - def get_mlm_property(self, prop: str) -> Optional[list[Any]]: + def get_mlm_property(self, prop: str) -> list[Any] | None: self._check_mlm_property(prop) return self.summaries.get_list(prop) @@ -251,7 +248,7 @@ class AssetMLModelExtension(MLModelExtension[pystac.Asset]): properties: dict[str, Any] """The :class:`~pystac.Asset` fields, including extension properties.""" - additional_read_properties: Optional[Iterable[dict[str, Any]]] = None + additional_read_properties: Iterable[dict[str, Any]] | None = None """If present, this will be a list containing 1 dictionary representing the properties of the owning :class:`~pystac.Item`.""" diff --git a/tests/conftest.py b/tests/conftest.py index 417a913..5688bd9 100644 --- a/tests/conftest.py +++ b/tests/conftest.py @@ -1,7 +1,7 @@ import glob import json import os -from typing import TYPE_CHECKING, Any, Dict, List, cast +from typing import TYPE_CHECKING, Any, cast import pystac import pytest @@ -18,7 +18,7 @@ JSON_SCHEMA_DIR = os.path.abspath(os.path.join(TEST_DIR, "../json-schema")) -def get_all_stac_item_examples() -> List[str]: +def get_all_stac_item_examples() -> list[str]: all_json = glob.glob("**/*.json", root_dir=EXAMPLES_DIR, recursive=True) all_geojson = glob.glob("**/*.geojson", root_dir=EXAMPLES_DIR, recursive=True) all_stac_items = [ @@ -39,7 +39,7 @@ def mlm_schema() -> JSON: @pytest.fixture(scope="session") def mlm_validator( request: "SubRequest", - mlm_schema: Dict[str, Any], + mlm_schema: dict[str, Any], ) -> pystac.validation.stac_validator.JsonSchemaSTACValidator: """ Update the :class:`pystac.validation.RegisteredValidator` with the local MLM JSON schema definition. @@ -58,10 +58,10 @@ def mlm_validator( @pytest.fixture -def mlm_example(request: "SubRequest") -> Dict[str, JSON]: +def mlm_example(request: "SubRequest") -> dict[str, JSON]: with open(os.path.join(EXAMPLES_DIR, request.param)) as example_file: data = json.load(example_file) - return cast(Dict[str, JSON], data) + return cast(dict[str, JSON], data) @pytest.fixture(name="eurosat_resnet") diff --git a/tests/test_schema.py b/tests/test_schema.py index 4755978..83cfe29 100644 --- a/tests/test_schema.py +++ b/tests/test_schema.py @@ -1,6 +1,6 @@ import copy import os -from typing import Any, Dict, List, cast +from typing import Any, cast import pystac import pytest @@ -19,9 +19,9 @@ ) def test_mlm_schema( mlm_validator: STACValidator, - mlm_example: Dict[str, JSON], + mlm_example: dict[str, JSON], ) -> None: - mlm_item = pystac.Item.from_dict(cast(Dict[str, Any], mlm_example)) + mlm_item = pystac.Item.from_dict(cast(dict[str, Any], mlm_example)) validated = pystac.validation.validate(mlm_item, validator=mlm_validator) assert len(validated) >= len(mlm_item.stac_extensions) # extra STAC core schemas assert SCHEMA_URI in validated @@ -34,7 +34,7 @@ def test_mlm_schema( ) def test_mlm_missing_bands_invalid_if_mlm_input_lists_bands( mlm_validator: STACValidator, - mlm_example: Dict[str, JSON], + mlm_example: dict[str, JSON], ) -> None: mlm_item = pystac.Item.from_dict(mlm_example) pystac.validation.validate(mlm_item, validator=mlm_validator) # ensure original is valid @@ -53,7 +53,7 @@ def test_mlm_missing_bands_invalid_if_mlm_input_lists_bands( ) def test_mlm_eo_bands_invalid_only_in_item_properties( mlm_validator: STACValidator, - mlm_example: Dict[str, JSON], + mlm_example: dict[str, JSON], ) -> None: mlm_item = pystac.Item.from_dict(mlm_example) pystac.validation.validate(mlm_item, validator=mlm_validator) # ensure original is valid @@ -72,7 +72,7 @@ def test_mlm_eo_bands_invalid_only_in_item_properties( ) def test_mlm_no_input_allowed_but_explicit_empty_array_required( mlm_validator: STACValidator, - mlm_example: Dict[str, JSON], + mlm_example: dict[str, JSON], ) -> None: mlm_data = copy.deepcopy(mlm_example) mlm_data["properties"]["mlm:input"] = [] # type: ignore @@ -92,7 +92,7 @@ def test_mlm_no_input_allowed_but_explicit_empty_array_required( ) def test_mlm_other_non_mlm_assets_allowed( mlm_validator: STACValidator, - mlm_example: Dict[str, JSON], + mlm_example: dict[str, JSON], ) -> None: mlm_data = copy.deepcopy(mlm_example) mlm_item = pystac.Item.from_dict(mlm_data) @@ -121,7 +121,7 @@ def test_mlm_other_non_mlm_assets_allowed( ) def test_mlm_at_least_one_asset_model( mlm_validator: STACValidator, - mlm_example: Dict[str, JSON], + mlm_example: dict[str, JSON], ) -> None: mlm_data = copy.deepcopy(mlm_example) mlm_item = pystac.Item.from_dict(mlm_data) @@ -163,7 +163,7 @@ def test_collection_include_all_items(mlm_example): """ This is only for self-validation, to make sure all examples are contained in the example STAC collection. """ - col_links: List[Dict[str, str]] = mlm_example["links"] + col_links: list[dict[str, str]] = mlm_example["links"] col_items = {os.path.basename(link["href"]) for link in col_links if link["rel"] == "item"} all_items = {os.path.basename(path) for path in get_all_stac_item_examples()} assert all_items == col_items, "Missing STAC Item examples in the example STAC Collection links." From fe169535d6fb098a16aa3d358a1fdffff916f21d Mon Sep 17 00:00:00 2001 From: Ryan Avery Date: Fri, 1 Nov 2024 10:52:23 -0400 Subject: [PATCH 24/83] update example to 1.0.0 --- examples/collection.json | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/examples/collection.json b/examples/collection.json index aff632b..0c596b4 100644 --- a/examples/collection.json +++ b/examples/collection.json @@ -1,5 +1,5 @@ { - "stac_version": "1.0.0-rc.1", + "stac_version": "1.0.0", "stac_extensions": [ "https://stac-extensions.github.io/item-assets/v1.0.0/schema.json" ], From 5a849052a9f86cc2d0a58ecdf58f72ce6102ef8d Mon Sep 17 00:00:00 2001 From: Ryan Avery Date: Fri, 1 Nov 2024 11:00:01 -0400 Subject: [PATCH 25/83] remove docker, point to uv docs, uv badge --- .dockerignore | 39 ----------------------------------- .github/dependabot.yml | 10 --------- CONTRIBUTING.md | 19 +++++++++-------- Makefile | 7 ------- README_STAC_MODEL.md | 10 ++++----- docker/Dockerfile | 25 ---------------------- docker/README.md | 47 ------------------------------------------ 7 files changed, 15 insertions(+), 142 deletions(-) delete mode 100644 .dockerignore delete mode 100644 docker/Dockerfile delete mode 100644 docker/README.md diff --git a/.dockerignore b/.dockerignore deleted file mode 100644 index b6d76d3..0000000 --- a/.dockerignore +++ /dev/null @@ -1,39 +0,0 @@ -# Git -.git -.gitignore -.github - -# Docker -.dockerignore - -# IDE -.idea -.vscode - -# Byte-compiled / optimized / DLL files -__pycache__/ -**/__pycache__/ -*.pyc -*.pyo -*.pyd -.Python -*.py[cod] -*$py.class -.pytest_cache/ -..mypy_cache/ - -# poetry -.venv -requirements*.txt - -# C extensions -*.so - -# Virtual environment -.venv -venv - -.DS_Store -.AppleDouble -.LSOverride -._* diff --git a/.github/dependabot.yml b/.github/dependabot.yml index 8f872f1..ff92d7a 100644 --- a/.github/dependabot.yml +++ b/.github/dependabot.yml @@ -23,13 +23,3 @@ updates: commit-message: prefix: ":arrow_up:" open-pull-requests-limit: 5 - - - package-ecosystem: "docker" - directory: "/docker" - schedule: - interval: "monthly" - allow: - - dependency-type: "all" - commit-message: - prefix: ":arrow_up:" - open-pull-requests-limit: 5 diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md index a9adfdb..dc2ba61 100644 --- a/CONTRIBUTING.md +++ b/CONTRIBUTING.md @@ -2,23 +2,24 @@ ## Project setup -1. If you don't have `Poetry` installed run: +1. If you don't have `uv` installed run: ```bash -make poetry-install +make setup ``` -> This installs Poetry as a [standalone application][poetry-install].
-> For more details, see also the [Poetry Documentation][poetry-docs].
-> If you prefer, you can simply install it inside your virtual environment. +> This installs uv as a [standalone application][uv-install].
+> For more details, see also the [uv documentation][uv-docs].
-2. Initialize project dependencies with poetry and install `pre-commit` hooks: +2. Initialize project dependencies with uv and install `pre-commit` hooks: ```bash make install-dev make pre-commit-install ``` +This will install project dependencies into the currently active environment. If you would like to use uv's default behavior of managing a project-scoped environment, use uv commands directly to install dependencies. `uv sync` will install dependencies and dev dependencies in `.venv` and update the `uv.lock`. + ## PR submission Before submitting your code please do the following steps: @@ -85,7 +86,7 @@ git push -u origin your-branch ### Building a new version of `stac-model` - Apply any relevant changes and `CHANGELOG.md` entries in a PR that modifies `stac-model`. -- Bump the version with `poetry version `. +- Bump the version with `bumpversion bump `. - You can pass the new version explicitly, or a rule such as `major`, `minor`, or `patch`.
For more details, refer to the [Semantic Versions][semver] standard; - Once CI validation succeeded, merge the corresponding PR branch. @@ -110,6 +111,6 @@ You can also share how the ML Model extension does or does not serve your needs with us in the GitHub Discussions or raise Issues for bugs. -[poetry-install]: https://github.com/python-poetry/install.python-poetry.org -[poetry-docs]: https://python-poetry.org/docs/ +[uv-install]: https://docs.astral.sh/uv/getting-started/installation/ +[uv-docs]: https://docs.astral.sh/uv/ [semver]: https://semver.org/ diff --git a/Makefile b/Makefile index 90cc310..29f54cc 100644 --- a/Makefile +++ b/Makefile @@ -68,13 +68,6 @@ mypy: setup .PHONY: check-mypy check-mypy: mypy -# NOTE: -# purposely running with docker rather than python package due to conflicting dependencies -# see https://github.com/citation-file-format/cffconvert/issues/292 -.PHONY: check-citation -check-citation: - docker run --rm -v $(ACTIVEPYTHON)/CITATION.cff:/app/CITATION.cff citationcff/cffconvert --validate - .PHONY: check-safety check-safety: setup uv run --python $(ACTIVEPYTHON) safety check --full-report diff --git a/README_STAC_MODEL.md b/README_STAC_MODEL.md index 897f5cd..b60a37f 100644 --- a/README_STAC_MODEL.md +++ b/README_STAC_MODEL.md @@ -12,7 +12,7 @@ [![Contributions Welcome][bp8]][bp9] -[![Poetry][bp11]][bp12] +[![uv][bp11]][bp12] [![Pre-commit][bp15]][bp16] [![Semantic versions][blic3]][bp5] [![Pipelines][bscm6]][bscm7] @@ -30,10 +30,10 @@ _A PydanticV2 and PySTAC validation and serialization library for the STAC ML Mo pip install -U stac-model ``` -or install with `Poetry`: +or install with `uv`: ```shell -poetry add stac-model +uv add stac-model ``` Then you can run @@ -73,8 +73,8 @@ See [LICENSE][blic2] for more details. [bp7]: https://kutt.it/7fYqQl [bp8]: https://img.shields.io/static/v1.svg?label=Contributions&message=Welcome&color=0059b3&style=for-the-badge [bp9]: https://github.com/stac-extensions/mlm/blob/main/CONTRIBUTING.md -[bp11]: https://img.shields.io/endpoint?url=https://python-poetry.org/badge/v0.json&style=for-the-badge -[bp12]: https://python-poetry.org/ +[bp11]: https://img.shields.io/endpoint?url=https://raw.githubusercontent.com/astral-sh/uv/main/assets/badge/v0.json +[bp12]: https://docs.astral.sh/uv/ [bp15]: https://img.shields.io/badge/pre--commit-enabled-brightgreen?logo=pre-commit&logoColor=white&style=for-the-badge [bp16]: https://github.com/stac-extensions/mlm/blob/main/.pre-commit-config.yaml diff --git a/docker/Dockerfile b/docker/Dockerfile deleted file mode 100644 index 6972457..0000000 --- a/docker/Dockerfile +++ /dev/null @@ -1,25 +0,0 @@ -FROM python:3.11-slim-buster - -ENV LANG=C.UTF-8 \ - LC_ALL=C.UTF-8 \ - PATH="${PATH}:/root/.poetry/bin" - -RUN apt-get update && \ - apt-get install -y --no-install-recommends \ - curl \ - && rm -rf /var/lib/apt/lists/* - -COPY pyproject.toml ./ - -# Install Poetry -RUN curl -sSL https://install.python-poetry.org | POETRY_HOME=/opt/poetry python && \ - cd /usr/local/bin && \ - ln -s /opt/poetry/bin/poetry && \ - poetry config virtualenvs.create false - -# Allow installing dev dependencies to run tests -ARG INSTALL_DEV=false -RUN bash -c "if [ $INSTALL_DEV == 'true' ] ; then poetry install --no-root ; else poetry install --no-root --no-dev ; fi" - -CMD mkdir -p /workspace -WORKDIR /workspace diff --git a/docker/README.md b/docker/README.md deleted file mode 100644 index 614d5b4..0000000 --- a/docker/README.md +++ /dev/null @@ -1,47 +0,0 @@ -# Docker for stac-model - -## Installation - -To create Docker you need to run: - -```bash -make docker-build -``` - -which is equivalent to: - -```bash -make docker-build VERSION=latest -``` - -You may provide name and version for the image. -Default name is `IMAGE := stac_model`. -Default version is `VERSION := latest`. - -```bash -make docker-build IMAGE=some_name VERSION=0.1.0 -``` - -## Usage - -```bash -docker run -it --rm \ - -v $(pwd):/workspace \ - stac_model bash -``` - -## How to clean up - -To uninstall docker image run `make docker-remove` with `VERSION`: - -```bash -make docker-remove VERSION=0.1.0 -``` - -you may also choose the image name - -```bash -make docker-remove IMAGE=some_name VERSION=latest -``` - -If you want to clean all, including `build` and `pycache` run `make cleanup` From 15b7aae4320777f792ab54abf7cd617331244618 Mon Sep 17 00:00:00 2001 From: Ryan Avery Date: Fri, 1 Nov 2024 11:03:06 -0400 Subject: [PATCH 26/83] line length fix --- CONTRIBUTING.md | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md index dc2ba61..d1a29ab 100644 --- a/CONTRIBUTING.md +++ b/CONTRIBUTING.md @@ -18,7 +18,9 @@ make install-dev make pre-commit-install ``` -This will install project dependencies into the currently active environment. If you would like to use uv's default behavior of managing a project-scoped environment, use uv commands directly to install dependencies. `uv sync` will install dependencies and dev dependencies in `.venv` and update the `uv.lock`. +This will install project dependencies into the currently active environment. If you would like to +use uv's default behavior of managing a project-scoped environment, use uv commands directly to +install dependencies. `uv sync` will install dependencies and dev dependencies in `.venv` and update the `uv.lock`. ## PR submission From a94aaa5711d9f12dd647a226f402ecbb954c9a81 Mon Sep 17 00:00:00 2001 From: Ryan Avery Date: Fri, 1 Nov 2024 11:42:43 -0400 Subject: [PATCH 27/83] fix style of uv badge and remove github pages badge --- README_STAC_MODEL.md | 6 +----- 1 file changed, 1 insertion(+), 5 deletions(-) diff --git a/README_STAC_MODEL.md b/README_STAC_MODEL.md index b60a37f..32a9f4a 100644 --- a/README_STAC_MODEL.md +++ b/README_STAC_MODEL.md @@ -8,7 +8,6 @@ [![PyPI Release][bp3]][bp2] [![Repository][bscm1]][bp4] [![Releases][bscm2]][bp5] -[![Docs][bdoc1]][bdoc2] [![Contributions Welcome][bp8]][bp9] @@ -73,7 +72,7 @@ See [LICENSE][blic2] for more details. [bp7]: https://kutt.it/7fYqQl [bp8]: https://img.shields.io/static/v1.svg?label=Contributions&message=Welcome&color=0059b3&style=for-the-badge [bp9]: https://github.com/stac-extensions/mlm/blob/main/CONTRIBUTING.md -[bp11]: https://img.shields.io/endpoint?url=https://raw.githubusercontent.com/astral-sh/uv/main/assets/badge/v0.json +[bp11]: https://img.shields.io/endpoint?url=https://raw.githubusercontent.com/astral-sh/uv/main/assets/badge/v0.json&style=for-the-badge [bp12]: https://docs.astral.sh/uv/ [bp15]: https://img.shields.io/badge/pre--commit-enabled-brightgreen?logo=pre-commit&logoColor=white&style=for-the-badge @@ -95,6 +94,3 @@ See [LICENSE][blic2] for more details. [hub6]: https://docs.github.com/en/code-security/dependabot [hub8]: https://github.com/stac-extensions/mlm/blob/main/.github/release-drafter.yml [hub9]: https://github.com/stac-extensions/mlm/blob/main/.github/.stale.yml - -[bdoc1]: https://img.shields.io/badge/docs-github%20pages-0a507a?style=for-the-badge -[bdoc2]: https://github.com/stac-extensions/mlm/blob/main/README_STAC_MODEL.md From ef82f26203cac288e4bccf365a2d3211d9abb3f3 Mon Sep 17 00:00:00 2001 From: Francis Charette Migneault Date: Fri, 1 Nov 2024 17:13:18 -0400 Subject: [PATCH 28/83] fix mlm:artifact_type check missing + update corresponding tests/examples (fixes #42) --- CHANGELOG.md | 4 +- README.md | 16 ++--- examples/item_bands_expression.json | 1 + examples/item_basic.json | 3 +- examples/item_eo_and_raster_bands.json | 2 +- examples/item_eo_bands.json | 1 + examples/item_eo_bands_summarized.json | 2 +- examples/item_multi_io.json | 1 + examples/item_raster_bands.json | 1 + json-schema/schema.json | 89 +++++++++++++++++++------- stac_model/examples.py | 1 + tests/test_schema.py | 37 ++++++++--- 12 files changed, 114 insertions(+), 44 deletions(-) diff --git a/CHANGELOG.md b/CHANGELOG.md index 116e1d3..93449c2 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -20,7 +20,9 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0 - n/a ### Fixed -- n/a +- Fix missing `mlm:artifact_type` property check for a Model Asset definition + (fixes ). + The `mlm:artifact_type` is now mutually and exclusively required by the corresponding Asset with `mlm:model` role. ## [v1.3.0](https://github.com/stac-extensions/mlm/tree/v1.3.0) diff --git a/README.md b/README.md index ad10344..d55685e 100644 --- a/README.md +++ b/README.md @@ -600,13 +600,13 @@ In order to provide more context, the following roles are also recommended were ### Model Asset -| Field Name | Type | Description | -|-------------------|-------------------------------------------|--------------------------------------------------------------------------------------------------| -| title | string | Description of the model asset. | -| href | string | URI to the model artifact. | -| type | string | The media type of the artifact (see [Model Artifact Media-Type](#model-artifact-media-type). | -| roles | \[string] | **REQUIRED** Specify `mlm:model`. Can include `["mlm:weights", "mlm:checkpoint"]` as applicable. | -| mlm:artifact_type | [Artifact Type Enum](#artifact-type-enum) | Specifies the kind of model artifact. Typically related to a particular ML framework. | +| Field Name | Type | Description | +|-------------------|---------------------------------|--------------------------------------------------------------------------------------------------| +| title | string | Description of the model asset. | +| href | string | URI to the model artifact. | +| type | string | The media type of the artifact (see [Model Artifact Media-Type](#model-artifact-media-type). | +| roles | \[string] | **REQUIRED** Specify `mlm:model`. Can include `["mlm:weights", "mlm:checkpoint"]` as applicable. | +| mlm:artifact_type | [Artifact Type](#artifact-type) | Specifies the kind of model artifact. Typically related to a particular ML framework. | Recommended Asset `roles` include `mlm:weights` or `mlm:checkpoint` for model weights that need to be loaded by a model definition and `mlm:compiled` for models that can be loaded directly without an intermediate model definition. @@ -642,7 +642,7 @@ official. In order to validate the specific framework and artifact type employed [iana-media-type]: https://www.iana.org/assignments/media-types/media-types.xhtml -#### Artifact Type Enum +#### Artifact Type This value can be used to provide additional details about the specific model artifact being described. For example, PyTorch offers [various strategies][pytorch-frameworks] for providing model definitions, diff --git a/examples/item_bands_expression.json b/examples/item_bands_expression.json index ea5d690..956f417 100644 --- a/examples/item_bands_expression.json +++ b/examples/item_bands_expression.json @@ -150,6 +150,7 @@ "mlm:model", "mlm:weights" ], + "mlm:artifact_type": "torch.save", "$comment": "Following 'eo:bands' is required to fulfil schema validation of 'eo' extension.", "eo:bands": [ { diff --git a/examples/item_basic.json b/examples/item_basic.json index 4ce3c24..3806446 100644 --- a/examples/item_basic.json +++ b/examples/item_basic.json @@ -120,7 +120,8 @@ "type": "text/html", "roles": [ "mlm:model" - ] + ], + "mlm:artifact_type": "torch.save" } }, "links": [ diff --git a/examples/item_eo_and_raster_bands.json b/examples/item_eo_and_raster_bands.json index d3fe6bf..8469f91 100644 --- a/examples/item_eo_and_raster_bands.json +++ b/examples/item_eo_and_raster_bands.json @@ -497,6 +497,7 @@ "mlm:model", "mlm:weights" ], + "mlm:artifact_type": "torch.save", "$comment": "Following 'eo:bands' is required to fulfil schema validation of 'eo' extension.", "eo:bands": [ { @@ -546,7 +547,6 @@ "description": "Source code to run the model.", "type": "text/x-python", "roles": [ - "mlm:model", "code", "metadata" ] diff --git a/examples/item_eo_bands.json b/examples/item_eo_bands.json index 23656a1..d02313e 100644 --- a/examples/item_eo_bands.json +++ b/examples/item_eo_bands.json @@ -273,6 +273,7 @@ "mlm:model", "mlm:weights" ], + "mlm:artifact_type": "torch.save", "$comment": "Following 'eo:bands' is required to fulfil schema validation of 'eo' extension.", "eo:bands": [ { diff --git a/examples/item_eo_bands_summarized.json b/examples/item_eo_bands_summarized.json index 8e20a17..8864505 100644 --- a/examples/item_eo_bands_summarized.json +++ b/examples/item_eo_bands_summarized.json @@ -366,6 +366,7 @@ "mlm:model", "mlm:weights" ], + "mlm:artifact_type": "torch.save", "$comment": "Following 'eo:bands' is required to fulfil schema validation of 'eo' extension.", "eo:bands": [ { @@ -415,7 +416,6 @@ "description": "Source code to run the model.", "type": "text/x-python", "roles": [ - "mlm:model", "code", "metadata" ] diff --git a/examples/item_multi_io.json b/examples/item_multi_io.json index 14c63f9..6db9aaa 100644 --- a/examples/item_multi_io.json +++ b/examples/item_multi_io.json @@ -228,6 +228,7 @@ "mlm:model", "mlm:weights" ], + "mlm:artifact_type": "torch.save", "raster:bands": [ { "name": "B02 - blue", diff --git a/examples/item_raster_bands.json b/examples/item_raster_bands.json index b97b06e..4e83bbf 100644 --- a/examples/item_raster_bands.json +++ b/examples/item_raster_bands.json @@ -216,6 +216,7 @@ "mlm:model", "mlm:weights" ], + "mlm:artifact_type": "torch.save", "raster:bands": [ { "name": "B01", diff --git a/json-schema/schema.json b/json-schema/schema.json index ff21357..6b662f0 100644 --- a/json-schema/schema.json +++ b/json-schema/schema.json @@ -63,8 +63,12 @@ "$ref": "#/$defs/AnyBandsRef" }, { - "$comment": "Schema to validate model role requirement.", + "$comment": "Schema to validate that at least one Asset defines a model role.", "$ref": "#/$defs/AssetModelRoleMinimumOneDefinition" + }, + { + "$comment": "Schema to validate that the Asset model properties are mutually exclusive to the model role.", + "$ref": "#/$defs/AssetModelRequiredProperties" } ] } @@ -369,6 +373,15 @@ "type": "string", "pattern": "^(0|[1-9]\\d*)\\.(0|[1-9]\\d*)\\.(0|[1-9]\\d*)(?:-((?:0|[1-9]\\d*|\\d*[a-zA-Z-][0-9a-zA-Z-]*)(?:\\.(?:0|[1-9]\\d*|\\d*[a-zA-Z-][0-9a-zA-Z-]*))*))?(?:\\+([0-9a-zA-Z-]+(?:\\.[0-9a-zA-Z-]+)*))?$" }, + "mlm:artifact_type": { + "type": "string", + "minLength": 1, + "examples": [ + "torch.save", + "torch.jit.save", + "torch.export.save" + ] + }, "mlm:tasks": { "type": "array", "uniqueItems": true, @@ -729,6 +742,57 @@ "DataType": { "$ref": "https://stac-extensions.github.io/raster/v1.1.0/schema.json#/definitions/bands/items/properties/data_type" }, + "HasArtifactType": { + "$comment": "Used to check the artifact type property that is required by a Model Asset annotated by 'mlm:model' role.", + "type": "object", + "required": [ + "mlm:artifact_type" + ], + "properties": { + "mlm:artifact_type": { + "$ref": "#/$defs/mlm:artifact_type" + } + } + }, + "AssetModelRole": { + "$comment": "Used to check the presence of 'mlm:model' role required by a Model Asset.", + "type": "object", + "required": [ + "roles" + ], + "properties": { + "roles": { + "type": "array", + "contains": { + "const": "mlm:model" + }, + "minItems": 1 + } + } + }, + "AssetModelRequiredProperties": { + "$comment": "Asset containing the model definition must indicate both the 'mlm:model' role and an artifact type.", + "required": [ + "assets" + ], + "properties": { + "assets": { + "additionalProperties": { + "if": { + "$ref": "#/$defs/AssetModelRole" + }, + "then": { + "$ref": "#/$defs/HasArtifactType" + }, + "else": { + "not": { + "$ref": "#/$defs/HasArtifactType" + } + } + } + } + } + }, "AssetModelRoleMinimumOneDefinition": { "$comment": "At least one Asset must provide the model definition indicated by the 'mlm:model' role.", "required": [ @@ -739,15 +803,7 @@ "properties": { "assets": { "additionalProperties": { - "properties": { - "roles": { - "type": "array", - "items": { - "const": "mlm:model" - }, - "minItems": 1 - } - } + "$ref": "#/$defs/AssetModelRole" } } } @@ -775,19 +831,6 @@ } ] }, - "AssetModelRole": { - "required": [ - "roles" - ], - "properties": { - "roles": { - "contains": { - "type": "string", - "const": "mlm:model" - } - } - } - }, "ModelBands": { "description": "List of bands (if any) that compose the input. Band order represents the index position of the bands.", "$comment": "No 'minItems' here to support model inputs not using any band (other data source).", diff --git a/stac_model/examples.py b/stac_model/examples.py index d882623..2d8cc41 100644 --- a/stac_model/examples.py +++ b/stac_model/examples.py @@ -128,6 +128,7 @@ def eurosat_resnet() -> ItemMLModelExtension: "mlm:weights", "data", ], + extra_fields={"mlm:artifact_type": "torch.save"} ), "source_code": pystac.Asset( title="Model implementation.", diff --git a/tests/test_schema.py b/tests/test_schema.py index 4755978..9f0acdd 100644 --- a/tests/test_schema.py +++ b/tests/test_schema.py @@ -119,25 +119,44 @@ def test_mlm_other_non_mlm_assets_allowed( ["item_basic.json"], indirect=True, ) +@pytest.mark.parametrize( + ["model_asset_extras", "is_valid"], + [ + ({"roles": ["checkpoint"]}, False), + ({"roles": ["checkpoint", "mlm:model"]}, False), + ({"roles": ["checkpoint"], "mlm:artifact_type": "test"}, False), + ({"roles": ["checkpoint", "mlm:model"], "mlm:artifact_type": "test"}, True), + ] +) def test_mlm_at_least_one_asset_model( mlm_validator: STACValidator, mlm_example: Dict[str, JSON], + model_asset_extras: Dict[str, Any], + is_valid: bool, ) -> None: mlm_data = copy.deepcopy(mlm_example) mlm_item = pystac.Item.from_dict(mlm_data) pystac.validation.validate(mlm_item, validator=mlm_validator) # self-check valid beforehand - mlm_data["assets"] = { # needs at least 1 asset with role 'mlm:model' - "model": { - "type": "application/octet-stream; application=pytorch", - "href": "https://example.com/sample/checkpoint.pt", - "roles": ["checkpoint"], - "title": "Model Weights Checkpoint", - } + mlm_model = { + "type": "application/octet-stream; application=pytorch", + "href": "https://example.com/sample/checkpoint.pt", + "title": "Model Weights Checkpoint", } - with pytest.raises(pystac.errors.STACValidationError): - mlm_item = pystac.Item.from_dict(mlm_data) + mlm_model.update(model_asset_extras) + mlm_data["assets"] = { + "model": mlm_model + } + mlm_item = pystac.Item.from_dict(mlm_data) + if is_valid: pystac.validation.validate(mlm_item, validator=mlm_validator) + else: + with pytest.raises(pystac.errors.STACValidationError) as exc: + pystac.validation.validate(mlm_item, validator=mlm_validator) + assert exc.value.source[0].schema["$comment"] in [ + "At least one Asset must provide the model definition indicated by the 'mlm:model' role.", + "Used to check the artifact type property that is required by a Model Asset annotated by 'mlm:model' role." + ] def test_model_metadata_to_dict(eurosat_resnet): From caa85971db6c14981b438eef38ff6859271a0cdc Mon Sep 17 00:00:00 2001 From: Francis Charette Migneault Date: Fri, 1 Nov 2024 17:19:29 -0400 Subject: [PATCH 29/83] ignore typing in tests --- tests/test_schema.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tests/test_schema.py b/tests/test_schema.py index 9f0acdd..f9a77d1 100644 --- a/tests/test_schema.py +++ b/tests/test_schema.py @@ -153,7 +153,7 @@ def test_mlm_at_least_one_asset_model( else: with pytest.raises(pystac.errors.STACValidationError) as exc: pystac.validation.validate(mlm_item, validator=mlm_validator) - assert exc.value.source[0].schema["$comment"] in [ + assert exc.value.source[0].schema["$comment"] in [ # type: ignore "At least one Asset must provide the model definition indicated by the 'mlm:model' role.", "Used to check the artifact type property that is required by a Model Asset annotated by 'mlm:model' role." ] From b750dec7aa57045f06caca89b989e8d5e65410ae Mon Sep 17 00:00:00 2001 From: Francis Charette Migneault Date: Fri, 1 Nov 2024 17:21:07 -0400 Subject: [PATCH 30/83] ignore test typing --- tests/test_schema.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tests/test_schema.py b/tests/test_schema.py index 3958b55..fec5660 100644 --- a/tests/test_schema.py +++ b/tests/test_schema.py @@ -40,7 +40,7 @@ def test_mlm_no_undefined_prefixed_field( mlm_item = pystac.Item.from_dict(mlm_data) pystac.validation.validate(mlm_item, validator=mlm_validator) # ensure original is valid - mlm_data["properties"]["mlm:unknown"] = "random" + mlm_data["properties"]["mlm:unknown"] = "random" # type: ignore with pytest.raises(pystac.errors.STACValidationError) as exc: mlm_item = pystac.Item.from_dict(mlm_data) pystac.validation.validate(mlm_item, validator=mlm_validator) From 24140750e80214a4492429f0490d278150705fe4 Mon Sep 17 00:00:00 2001 From: Francis Charette Migneault Date: Fri, 1 Nov 2024 17:22:39 -0400 Subject: [PATCH 31/83] more test typing ignore --- tests/test_schema.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tests/test_schema.py b/tests/test_schema.py index f9a77d1..3ba15ec 100644 --- a/tests/test_schema.py +++ b/tests/test_schema.py @@ -145,7 +145,7 @@ def test_mlm_at_least_one_asset_model( } mlm_model.update(model_asset_extras) mlm_data["assets"] = { - "model": mlm_model + "model": mlm_model # type: ignore } mlm_item = pystac.Item.from_dict(mlm_data) if is_valid: From 7f70ce47d9fb4d17b29fb7b2ad630c6a67d05acc Mon Sep 17 00:00:00 2001 From: Francis Charette-Migneault Date: Mon, 4 Nov 2024 01:25:53 -0500 Subject: [PATCH 32/83] add missing close parenthesis --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index ad10344..a8b52ff 100644 --- a/README.md +++ b/README.md @@ -342,7 +342,7 @@ properties of the model. | Field Name | Type | Description | |------------|--------|----------------------------------------------------------------------------------------------------------------------------------------| -| name | string | **REQUIRED** Name of the band referring to an extended band definition (see [Bands](#bands-and-statistics). | +| name | string | **REQUIRED** Name of the band referring to an extended band definition (see [Bands](#bands-and-statistics) detiails). | | format | string | The type of expression that is specified in the `expression` property. | | expression | \* | An expression compliant with the `format` specified. The expression can be applied to any data type and depends on the `format` given. | From 43c0aaf87fdceefbf57338d4fe573f9c320d2f9e Mon Sep 17 00:00:00 2001 From: Francis Charette-Migneault Date: Mon, 4 Nov 2024 01:26:21 -0500 Subject: [PATCH 33/83] Update README.md --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index a8b52ff..1f4c05b 100644 --- a/README.md +++ b/README.md @@ -342,7 +342,7 @@ properties of the model. | Field Name | Type | Description | |------------|--------|----------------------------------------------------------------------------------------------------------------------------------------| -| name | string | **REQUIRED** Name of the band referring to an extended band definition (see [Bands](#bands-and-statistics) detiails). | +| name | string | **REQUIRED** Name of the band referring to an extended band definition (see [Bands](#bands-and-statistics) details). | | format | string | The type of expression that is specified in the `expression` property. | | expression | \* | An expression compliant with the `format` specified. The expression can be applied to any data type and depends on the `format` given. | From 56a48f54a7ff0c06e24482a51af6f65a6f21aa51 Mon Sep 17 00:00:00 2001 From: Francis Charette-Migneault Date: Mon, 4 Nov 2024 01:30:11 -0500 Subject: [PATCH 34/83] fix invalid dimensions of MLM inputs/outputs example --- examples/item_bands_expression.json | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/examples/item_bands_expression.json b/examples/item_bands_expression.json index ea5d690..edd4a41 100644 --- a/examples/item_bands_expression.json +++ b/examples/item_bands_expression.json @@ -86,7 +86,7 @@ "input": { "shape": [ -1, - 13, + 4, 64, 64 ], @@ -110,7 +110,7 @@ "result": { "shape": [ -1, - 10 + 2 ], "dim_order": [ "batch", From 768ac13b80c187a408cbc237350d005de5c58c66 Mon Sep 17 00:00:00 2001 From: Francis Charette-Migneault Date: Mon, 4 Nov 2024 01:44:43 -0500 Subject: [PATCH 35/83] fix typo --- best-practices.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/best-practices.md b/best-practices.md index dcdf389..d7e1675 100644 --- a/best-practices.md +++ b/best-practices.md @@ -116,7 +116,7 @@ A potential representation of a STAC Asset could be as follows: Furthermore, the STAC Item representing the derived product could also include a [Link Object](https://github.com/radiantearth/stac-spec/tree/master/item-spec/item-spec.md#link-object) referring back to the MLM definition using `rel: derived_from`, as described in -[MLM Relation Types](README.md#relation-types). Such a link would like something like the following: +[MLM Relation Types](README.md#relation-types). Such a link would look something like the following: ```json { From 0cc32e98c6c0aeff1ebf09c4a141d36a9c3cfb95 Mon Sep 17 00:00:00 2001 From: Francis Charette Migneault Date: Tue, 5 Nov 2024 11:11:54 -0500 Subject: [PATCH 36/83] update scaling -> value_scaling and corresponding definitions --- CHANGELOG.md | 31 ++++++++++-------- README.md | 45 +++++++++++++------------- examples/item_eo_and_raster_bands.json | 2 +- examples/item_eo_bands.json | 2 +- examples/item_eo_bands_summarized.json | 2 +- examples/item_multi_io.json | 2 +- examples/item_raster_bands.json | 2 +- json-schema/schema.json | 14 ++++---- stac_model/examples.py | 8 ++--- stac_model/input.py | 36 ++++++++++----------- tests/test_schema.py | 2 +- 11 files changed, 75 insertions(+), 71 deletions(-) diff --git a/CHANGELOG.md b/CHANGELOG.md index 50951dd..8e606f0 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -8,16 +8,18 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0 ## [Unreleased](https://github.com/stac-extensions/mlm/tree/main) ### Added -- Add explicit check of `scaling` sub-fields `minimum`, `maximum`, `mean`, `stddev`, etc. for corresponding `type` - values `min-max` and `z-score` that depend on it. -- Allow different `scaling` operations per band/channel/dimension as needed by the model. -- Allow a `processing:expression` for a band/channel/dimension-specific `scaling` operation, granting more flexibility - in the definition of input preparation in contrast to having it applied for the entire input (but still possible). +- Add explicit check of `value_scaling` sub-fields `minimum`, `maximum`, `mean`, `stddev`, etc. for + corresponding `type` values `min-max` and `z-score` that depend on it. +- Allow different `value_scaling` operations per band/channel/dimension as needed by the model. +- Allow a `processing:expression` for a band/channel/dimension-specific `value_scaling` operation, + granting more flexibility in the definition of input preparation in contrast to having it applied + for the entire input (but still possible). ### Changed -- Moved `norm_type` to `scaling` object to better reflect the expected operation, which could be another operation - than what is typically known as "normalization" or "standardization" techniques in machine learning. -- Moved `statistics` to `scaling` object to better reflect their mutual `type` and additional properties dependencies. +- Moved `norm_type` to `value_scaling` object to better reflect the expected operation, which could be another + operation than what is typically known as "normalization" or "standardization" techniques in machine learning. +- Moved `statistics` to `value_scaling` object to better reflect their mutual `type` and additional + properties dependencies. ### Deprecated - n/a @@ -25,10 +27,11 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0 ### Removed - Removed `norm_type` enum values that were ambiguous regarding their expected result. Instead, a `processing:expression` should be employed to explicitly define the calculation they represent. -- Removed `norm_clip` property. It is now represented under `scaling` objects with a corresponding `type` definition. +- Removed `norm_clip` property. It is now represented under `value_scaling` objects with a + corresponding `type` definition. - Removed `norm_by_channel` from `mlm:input` objects. If rescaling (previously normalization in the documentation) is a single value, broadcasting to the relevant bands should be performed implicitly. - Otherwise, the amount of `scaling` objects should match the number of bands or channels involved in the input. + Otherwise, the amount of `value_scaling` objects should match the number of bands or channels involved in the input. ### Fixed - n/a @@ -62,7 +65,7 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0 when a `mlm:input` references names in `bands` are now properly validated. - Fix the examples using `raster:bands` incorrectly defined in STAC Item properties. The correct use is for them to be defined under the STAC Asset using the `mlm:model` role. -- Fix the [EuroSAT ResNet pydantic example](./stac_model/examples.py) that incorrectly referenced some `bands` +- Fix the [EuroSAT ResNet pydantic example](stac_model/examples.py) that incorrectly referenced some `bands` in its `mlm:input` definition without providing any definition of those bands. The `eo:bands` properties have been added to the corresponding `model` Asset using the [`pystac.extensions.eo`](https://github.com/stac-utils/pystac/blob/main/pystac/extensions/eo.py) utilities. @@ -123,7 +126,7 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0 - more [Task Enum](README.md#task-enum) tasks - [Model Output Object](README.md#model-output-object) - batch_size and hardware summary -- [`mlm:accelerator`, `mlm:accelerator_constrained`, `mlm:accelerator_summary`](./README.md#accelerator-type-enum) +- [`mlm:accelerator`, `mlm:accelerator_constrained`, `mlm:accelerator_summary`](README.md#accelerator-type-enum) to specify hardware requirements for the model - Use common metadata [Asset Object](https://github.com/radiantearth/stac-spec/blob/master/collection-spec/collection-spec.md#asset-object) @@ -138,7 +141,7 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0 STAC Item properties (top-level, not nested) to allow better search support by STAC API. - reorganized `dlm:architecture` nested fields to exist at the top level of properties as `mlm:name`, `mlm:summary` and so on to provide STAC API search capabilities. -- replaced `normalization:mean`, etc. with [statistics](./README.md#bands-and-statistics) from STAC 1.1 common metadata +- replaced `normalization:mean`, etc. with [statistics](README.md#bands-and-statistics) from STAC 1.1 common metadata - added `pydantic` models for internal schema objects in `stac_model` package and published to PYPI - specified [rel_type](README.md#relation-types) to be `derived_from` and specify how model item or collection json should be named @@ -154,7 +157,7 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0 - any `dlm`-prefixed field or property ### Removed -- Data Object, replaced with [Model Input Object](./README.md#model-input-object) that uses the `name` field from +- Data Object, replaced with [Model Input Object](README.md#model-input-object) that uses the `name` field from the [common metadata band object][stac-bands] which also records `data_type` and `nodata` type ### Fixed diff --git a/README.md b/README.md index 96723f3..dec7a96 100644 --- a/README.md +++ b/README.md @@ -259,15 +259,15 @@ set to `true`, there would be no `accelerator` to contain against. To avoid conf ### Model Input Object -| Field Name | Type | Description | -|-------------------------|---------------------------------------------------------|-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| -| name | string | **REQUIRED** Name of the input variable defined by the model. If no explicit name is defined by the model, an informative name (e.g.: `"RGB Time Series"`) can be used instead. | -| bands | \[string \| [Model Band Object](#model-band-object)] | **REQUIRED** The raster band references used to train, fine-tune or perform inference with the model, which may be all or a subset of bands available in a STAC Item's [Band Object](#bands-and-statistics). If no band applies for one input, use an empty array. | -| input | [Input Structure Object](#input-structure-object) | **REQUIRED** The N-dimensional array definition that describes the shape, dimension ordering, and data type. | -| description | string | Additional details about the input such as describing its purpose or expected source that cannot be represented by other properties. | -| scaling | \[[Scaling Object](#bands-and-statistics)] \| null | Method to scale, normalize, or standardize the data inputs, or `null` if none applies. These values often correspond to dataset or sensor statistics employed for training the model, but can come from another source as needed by the model definition. Consider using `pre_processing_function` for custom implementations or more complex combinations. | -| resize_type | [Resize Enum](#resize-enum) \| null | High-level descriptor of the rescaling method to change image shape. Select an appropriate option or `null` when none applies. Consider using `pre_processing_function` for custom implementations or more complex combinations. | -| pre_processing_function | [Processing Expression](#processing-expression) \| null | Custom preprocessing function where normalization and rescaling, and any other significant operations takes place. The `pre_processing_function` should be applied over all available `bands`. For respective band operations, see [Model Band Object](#model-band-object). | +| Field Name | Type | Description | +|-------------------------|----------------------------------------------------------|--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| +| name | string | **REQUIRED** Name of the input variable defined by the model. If no explicit name is defined by the model, an informative name (e.g.: `"RGB Time Series"`) can be used instead. | +| bands | \[string \| [Model Band Object](#model-band-object)] | **REQUIRED** The raster band references used to train, fine-tune or perform inference with the model, which may be all or a subset of bands available in a STAC Item's [Band Object](#bands-and-statistics). If no band applies for one input, use an empty array. | +| input | [Input Structure Object](#input-structure-object) | **REQUIRED** The N-dimensional array definition that describes the shape, dimension ordering, and data type. | +| description | string | Additional details about the input such as describing its purpose or expected source that cannot be represented by other properties. | +| value_scaling | \[[Value Scaling Object](#value-scaling-object)] \| null | Method to scale, normalize, or standardize the data inputs values, across dimensions, per corresponding dimension index, or `null` if none applies. These values often correspond to dataset or sensor statistics employed for training the model, but can come from another source as needed by the model definition. Consider using `pre_processing_function` for custom implementations or more complex combinations. | +| resize_type | [Resize Enum](#resize-enum) \| null | High-level descriptor of the resize method to modify the input dimensions shape. Select an appropriate option or `null` when none applies. Consider using `pre_processing_function` for custom implementations or more complex combinations. | +| pre_processing_function | [Processing Expression](#processing-expression) \| null | Custom preprocessing function where rescaling and resize, and any other significant data preparation operations takes place. The `pre_processing_function` should be applied over all available `bands`. For respective band operations, see [Model Band Object](#model-band-object). | Fields that accept the `null` value can be considered `null` when omitted entirely for parsing purposes. However, setting `null` explicitly when this information is known by the model provider can help users understand @@ -333,9 +333,9 @@ any `bands` definition. In such case, defining statistics under `bands` would no ambiguous definitions. Finally, contrary to the "`statistics`" property name employed by [Band Statistics][stac-1.1-stats], MLM employs the -distinct name `scaling`, although similar `minimum`, `maximum`, etc. sub-fields are employed. +distinct name `value_scaling`, although similar `minimum`, `maximum`, etc. sub-fields are employed. This is done explicitly to disambiguate "informative" band statistics from "applied scaling operations" required -by the model inputs. This highlights the fact that `scaling` are not *necessarily* equal +by the model inputs. This highlights the fact that `value_scaling` are not *necessarily* equal to [Band Statistics][stac-1.1-stats] values, although they are often equal in practice due to the applicable value-range domains they represent. Also, this allows addressing special scaling cases, using additional properties unavailable from [Band Statistics][stac-1.1-stats], such as `value`-specific scaling @@ -420,7 +420,7 @@ Below are some notable common names recommended for use, but others can be emplo For example, a tensor of multiple RBG images represented as $B \times C \times H \times W$ should indicate `dim_order = ["batch", "channel", "height", "width"]`. -#### Scaling Object +#### Value Scaling Object Select one option from: @@ -439,22 +439,22 @@ When a scaling `type` approach is specified, it is expected that the parameters to perform their calculation are provided for the corresponding input dimension data. If none of the above values applies for a given dimension, `type: null` (literal `null`, not string) should be -used instead. If none of the input dimension require scaling, the entire definition can be defined as `scaling: null` -or be omitted entirely. +used instead. If none of the input dimension require scaling, the entire definition can be defined +as `value_scaling: null` or be omitted entirely. -When `scaling` is specified, the amount of objects defined in the array must match the size of -the bands/channels/dimensions described by the [Model Input](#model-input-object). However, the `scaling` array +When `value_scaling` is specified, the amount of objects defined in the array must match the size of +the bands/channels/dimensions described by the [Model Input](#model-input-object). However, the `value_scaling` array is allowed to contain a single object if the entire input must be rescaled using the same definition across all -dimensions. In such case, implicit broadcasting of the unique [Scaling Object](#scaling-object) should be +dimensions. In such case, implicit broadcasting of the unique [Value Scaling Object](#value-scaling-object) should be performed for all applicable dimensions when running inference with the model. If a custom scaling operation, or a combination of more complex operations (with or without [Resize](#resize-enum)), must be defined instead, a [Processing Expression](#processing-expression) reference can be specified in place of -the [Scaling Object](#scaling-object) of the respective input dimension, as shown below. +the [Value Scaling Object](#value-scaling-object) of the respective input dimension, as shown below. ```json { - "scaling": [ + "value_scaling": [ {"type": "min-max", "minimum": 0, "maximum": 255}, {"type": "clip", "minimum": 0, "maximum": 255}, {"type": "processing", "format": "gdal-calc", "expression": "A * logical_or( A<=177, A>=185 )"} @@ -467,7 +467,7 @@ This is because, depending on the [Model Input](#model-input-object) dimensions ambiguity regarding "how" and "where" such normalization functions must be applied against the input data. A custom mathematical expression should provide explicitly the data manipulation and normalization strategy. -In situations of very complex `scaling` operations, which cannot be represented by any of the previous definition, +In situations of very complex `value_scaling` operations, which cannot be represented by any of the previous definition, a `pre_processing_function` should be used instead (see [Model Input Object](#model-input-object)). #### Resize Enum @@ -488,7 +488,8 @@ See [OpenCV - Interpolation Flags][opencv-interpolation-flags] for details about the relevant methods. Equivalent methods from other packages are applicable as well. If none of the above values applies, `null` (literal, not string) can be used instead. -If a custom rescaling operation, or a combination of operations (with or without [Scaling](#scaling-object)), +If a custom rescaling operation, or a combination of operations +(with or without [Value Scaling](#value-scaling-object)), must be defined instead, consider using a [Processing Expression](#processing-expression) reference. [opencv-interpolation-flags]: https://docs.opencv.org/4.x/da/d54/group__imgproc__transform.html#ga5bb5a1fea74ea38e1a5445ca803ff121 @@ -536,7 +537,7 @@ the following formats are recommended as alternative scripts and function refere | result | [Result Structure Object](#result-structure-object) | **REQUIRED** The structure that describes the resulting output arrays/tensors from one model head. | | description | string | Additional details about the output such as describing its purpose or expected result that cannot be represented by other properties. | | classification:classes | \[[Class Object](#class-object)] | A list of class objects adhering to the [Classification Extension](https://github.com/stac-extensions/classification). | -| post_processing_function | [Processing Expression](#processing-expression) \| null | Custom postprocessing function where normalization and rescaling, and any other significant operations takes place. | +| post_processing_function | [Processing Expression](#processing-expression) \| null | Custom postprocessing function where normalization, rescaling, or any other significant operations takes place. | While only `tasks` is a required field, all fields are recommended for tasks that produce a fixed shape tensor and have output classes. Outputs that have variable dimensions, can define the `result` with the diff --git a/examples/item_eo_and_raster_bands.json b/examples/item_eo_and_raster_bands.json index ab521f1..3c9da1d 100644 --- a/examples/item_eo_and_raster_bands.json +++ b/examples/item_eo_and_raster_bands.json @@ -97,7 +97,7 @@ "data_type": "float32" }, "resize_type": null, - "scaling": [ + "value_scaling": [ { "type": "z-score", "mean": 1354.40546513, diff --git a/examples/item_eo_bands.json b/examples/item_eo_bands.json index cd820c8..569c54b 100644 --- a/examples/item_eo_bands.json +++ b/examples/item_eo_bands.json @@ -99,7 +99,7 @@ }, "norm_by_channel": true, "resize_type": null, - "scaling": [ + "value_scaling": [ { "type": "z-score", "mean": 1354.40546513, diff --git a/examples/item_eo_bands_summarized.json b/examples/item_eo_bands_summarized.json index d27cade..53c66ab 100644 --- a/examples/item_eo_bands_summarized.json +++ b/examples/item_eo_bands_summarized.json @@ -98,7 +98,7 @@ "data_type": "float32" }, "resize_type": null, - "scaling": [ + "value_scaling": [ { "type": "z-score", "mean": 1354.40546513, diff --git a/examples/item_multi_io.json b/examples/item_multi_io.json index e92fe6d..dce2b87 100644 --- a/examples/item_multi_io.json +++ b/examples/item_multi_io.json @@ -85,7 +85,7 @@ ], "data_type": "uint16" }, - "scaling": null, + "value_scaling": null, "resize_type": null }, { diff --git a/examples/item_raster_bands.json b/examples/item_raster_bands.json index 24b36a5..c52fd42 100644 --- a/examples/item_raster_bands.json +++ b/examples/item_raster_bands.json @@ -95,7 +95,7 @@ ], "data_type": "float32" }, - "scaling": null, + "value_scaling": null, "resize_type": null, "pre_processing_function": { "format": "python", diff --git a/json-schema/schema.json b/json-schema/schema.json index 76532d9..4040641 100644 --- a/json-schema/schema.json +++ b/json-schema/schema.json @@ -516,8 +516,8 @@ "type": "string", "minLength": 1 }, - "scaling": { - "$ref": "#/$defs/Scaling" + "value_scaling": { + "$ref": "#/$defs/ValueScaling" }, "resize_type": { "$ref": "#/$defs/ResizeType" @@ -639,7 +639,7 @@ ] } }, - "Scaling": { + "ValueScaling": { "oneOf": [ { "type": "null" @@ -648,12 +648,12 @@ "type": "array", "minItems": 1, "items": { - "$ref": "#/$defs/ScalingObject" + "$ref": "#/$defs/ValueScalingObject" } } ] }, - "ScalingObject": { + "ValueScalingObject": { "oneOf": [ { "type": "object", @@ -776,11 +776,11 @@ } }, { - "$ref": "#/$defs/ScalingProcessingExpression" + "$ref": "#/$defs/ValueScalingProcessingExpression" } ] }, - "ScalingProcessingExpression": { + "ValueScalingProcessingExpression": { "allOf": [ { "type": "object", diff --git a/stac_model/examples.py b/stac_model/examples.py index 497ae93..a41b233 100644 --- a/stac_model/examples.py +++ b/stac_model/examples.py @@ -7,7 +7,7 @@ from pystac.extensions.file import FileExtension from stac_model.base import ProcessingExpression -from stac_model.input import InputStructure, ModelInput, ScalingObject +from stac_model.input import InputStructure, ModelInput, ValueScalingObject from stac_model.output import MLMClassification, ModelOutput, ModelResult from stac_model.schema import ItemMLModelExtension, MLModelExtension, MLModelProperties @@ -63,9 +63,9 @@ def eurosat_resnet() -> ItemMLModelExtension: 761.30323499, 1231.58581042, ] - scaling = [ + value_scaling = [ cast( - ScalingObject, + ValueScalingObject, dict( type="z-score", mean=mean, @@ -79,7 +79,7 @@ def eurosat_resnet() -> ItemMLModelExtension: bands=band_names, input=input_struct, resize_type=None, - scaling=scaling, + value_scaling=value_scaling, pre_processing_function=ProcessingExpression( format="python", expression="torchgeo.datamodules.eurosat.EuroSATDataModule.collate_fn", diff --git a/stac_model/input.py b/stac_model/input.py index 20dc84e..7941a36 100644 --- a/stac_model/input.py +++ b/stac_model/input.py @@ -18,58 +18,58 @@ def validate_dimensions(self) -> Self: return self -class ScalingClipMin(MLMBaseModel): +class ValueScalingClipMin(MLMBaseModel): type: Literal["clip-min"] = "clip-min" minimum: Number -class ScalingClipMax(MLMBaseModel): +class ValueScalingClipMax(MLMBaseModel): type: Literal["clip-max"] = "clip-max" maximum: Number -class ScalingClip(MLMBaseModel): +class ValueScalingClip(MLMBaseModel): type: Literal["clip"] = "clip" minimum: Number maximum: Number -class ScalingMinMax(MLMBaseModel): +class ValueScalingMinMax(MLMBaseModel): type: Literal["min-max"] = "min-max" minimum: Number maximum: Number -class ScalingZScore(MLMBaseModel): +class ValueScalingZScore(MLMBaseModel): type: Literal["z-score"] = "z-score" mean: Number stddev: Number -class ScalingOffset(MLMBaseModel): +class ValueScalingOffset(MLMBaseModel): type: Literal["offset"] = "offset" value: Number -class ScalingScale(MLMBaseModel): +class ValueScalingScale(MLMBaseModel): type: Literal["scale"] = "scale" value: Number -class ScalingProcessingExpression(ProcessingExpression): +class ValueScalingProcessingExpression(ProcessingExpression): type: Literal["processing"] = "processing" -ScalingObject: TypeAlias = Optional[ +ValueScalingObject: TypeAlias = Optional[ Union[ - ScalingMinMax, - ScalingZScore, - ScalingClip, - ScalingClipMin, - ScalingClipMax, - ScalingOffset, - ScalingScale, - ScalingProcessingExpression, + ValueScalingMinMax, + ValueScalingZScore, + ValueScalingClip, + ValueScalingClipMin, + ValueScalingClipMax, + ValueScalingOffset, + ValueScalingScale, + ValueScalingProcessingExpression, ] ] @@ -138,6 +138,6 @@ class ModelInput(MLMBaseModel): ], ) input: InputStructure - scaling: Annotated[Optional[List[ScalingObject]], OmitIfNone] = None + value_scaling: Annotated[Optional[List[ValueScalingObject]], OmitIfNone] = None resize_type: Annotated[Optional[ResizeType], OmitIfNone] = None pre_processing_function: Optional[ProcessingExpression] = None diff --git a/tests/test_schema.py b/tests/test_schema.py index 73e9789..c7cc9fb 100644 --- a/tests/test_schema.py +++ b/tests/test_schema.py @@ -118,7 +118,7 @@ def test_mlm_input_scaling_combination( mlm_item = pystac.Item.from_dict(mlm_data) pystac.validation.validate(mlm_item, validator=mlm_validator) # ensure original is valid - mlm_data["properties"]["mlm:input"][0]["scaling"] = test_scaling # type: ignore + mlm_data["properties"]["mlm:input"][0]["value_scaling"] = test_scaling # type: ignore mlm_item = pystac.Item.from_dict(mlm_data) if is_valid: pystac.validation.validate(mlm_item, validator=mlm_validator) From 278b75acd0e4f42b3f0de234cf43e253caade4a4 Mon Sep 17 00:00:00 2001 From: Francis Charette Migneault Date: Tue, 5 Nov 2024 11:14:14 -0500 Subject: [PATCH 37/83] fix missing replace --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index 98f0735..265ac4d 100644 --- a/README.md +++ b/README.md @@ -339,7 +339,7 @@ by the model inputs. This highlights the fact that `value_scaling` are not *nece to [Band Statistics][stac-1.1-stats] values, although they are often equal in practice due to the applicable value-range domains they represent. Also, this allows addressing special scaling cases, using additional properties unavailable from [Band Statistics][stac-1.1-stats], such as `value`-specific scaling -(see [Scaling Object](#scaling-object) for more details). +(see [Value Scaling Object](#value-scaling-object) for more details). [stac-1.1-band]: https://github.com/radiantearth/stac-spec/blob/v1.1.0/commons/common-metadata.md#bands [stac-1.1-stats]: https://github.com/radiantearth/stac-spec/blob/v1.1.0/commons/common-metadata.md#statistics-object From 8471a496325eb115bb1ae066008fabf56c4a04e2 Mon Sep 17 00:00:00 2001 From: Ryan Avery Date: Tue, 5 Nov 2024 09:58:53 -0800 Subject: [PATCH 38/83] ignore line length for certain lists, remove remove command --- .github/workflows/test.yaml | 2 +- README_STAC_MODEL.md | 2 +- pyproject.toml | 2 +- stac_model/input.py | 26 ++++++++++++++++++++++++-- 4 files changed, 27 insertions(+), 5 deletions(-) diff --git a/.github/workflows/test.yaml b/.github/workflows/test.yaml index 03169a5..9d8e0ac 100644 --- a/.github/workflows/test.yaml +++ b/.github/workflows/test.yaml @@ -11,5 +11,5 @@ jobs: - uses: actions/checkout@v4 - run: | npm install - npm test npm list + npm test diff --git a/README_STAC_MODEL.md b/README_STAC_MODEL.md index 32a9f4a..0217e7e 100644 --- a/README_STAC_MODEL.md +++ b/README_STAC_MODEL.md @@ -29,7 +29,7 @@ _A PydanticV2 and PySTAC validation and serialization library for the STAC ML Mo pip install -U stac-model ``` -or install with `uv`: +or install with uv: ```shell uv add stac-model diff --git a/pyproject.toml b/pyproject.toml index 0bd5f97..39e68c9 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -86,7 +86,7 @@ stac-model = "stac_model.__main__:app" # NOTE: # Although these definitions are provided in this 'stac-model' project file, # they are actually intented for versioning the MLM specification itself. -# To version 'stac-model', use the 'bumpversion bump' operation. +# To version 'stac-model', use the 'bump-my-version bump' operation. # See also https://github.com/stac-extensions/mlm/blob/main/CONTRIBUTING.md#building-and-releasing current_version = "1.3.0" parse = "(?P\\d+)\\.(?P\\d+)\\.(?P\\d+)" diff --git a/stac_model/input.py b/stac_model/input.py index 3001941..b40a458 100644 --- a/stac_model/input.py +++ b/stac_model/input.py @@ -28,9 +28,31 @@ class MLMStatistic(MLMBaseModel): # FIXME: add 'Statistics' dep from raster ext valid_percent: Annotated[Number | None, OmitIfNone] = None -NormalizeType: TypeAlias = Literal["min-max", "z-score", "l1", "l2", "l2sqr", "hamming", "hamming2", "type-mask", "relative", "inf"] | None +NormalizeType: TypeAlias = Literal[ + "min-max", + "z-score", + "l1", + "l2", + "l2sqr", + "hamming", + "hamming2", + "type-mask", + "relative", + "inf", + ] | None # noqa: E501 -ResizeType: TypeAlias = Literal["crop", "pad", "interpolation-nearest", "interpolation-linear", "interpolation-cubic", "interpolation-area", "interpolation-lanczos4", "interpolation-max", "wrap-fill-outliers", "wrap-inverse-map"] | None +ResizeType: TypeAlias = Literal[ + "crop", + "pad", + "interpolation-nearest", + "interpolation-linear", + "interpolation-cubic", + "interpolation-area", + "interpolation-lanczos4", + "interpolation-max", + "wrap-fill-outliers", + "wrap-inverse-map", + ] | None # noqa: E501 class ModelBand(MLMBaseModel): From d7278bf79472ea105e15390f768985372e53ae24 Mon Sep 17 00:00:00 2001 From: Ryan Avery Date: Tue, 5 Nov 2024 10:01:05 -0800 Subject: [PATCH 39/83] make format --- CONTRIBUTING.md | 6 +++--- Makefile | 12 ------------ stac_model/input.py | 14 ++++++++++---- stac_model/schema.py | 9 +++------ 4 files changed, 16 insertions(+), 25 deletions(-) diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md index d1a29ab..47b5169 100644 --- a/CONTRIBUTING.md +++ b/CONTRIBUTING.md @@ -2,7 +2,7 @@ ## Project setup -1. If you don't have `uv` installed run: +1. If you don't have uv installed run: ```bash make setup @@ -18,8 +18,8 @@ make install-dev make pre-commit-install ``` -This will install project dependencies into the currently active environment. If you would like to -use uv's default behavior of managing a project-scoped environment, use uv commands directly to +This will install project dependencies into the currently active environment. If you would like to +use uv's default behavior of managing a project-scoped environment, use uv commands directly to install dependencies. `uv sync` will install dependencies and dev dependencies in `.venv` and update the `uv.lock`. ## PR submission diff --git a/Makefile b/Makefile index 29f54cc..4e51def 100644 --- a/Makefile +++ b/Makefile @@ -11,18 +11,6 @@ setup: echo "uv is already installed"; \ fi -.PHONY: remove -remove: - @read -p "Are you sure you want to remove uv and all its data? [y/N] " confirm && \ - if [ "$$confirm" = "y" ]; then \ - uv cache clean; \ - rm -r "$$(uv python dir)"; \ - rm -r "$$(uv tool dir)"; \ - rm ~/.cargo/bin/uv ~/.cargo/bin/uvx; \ - else \ - echo "Operation cancelled."; \ - fi - .PHONY: publish publish: uv publish --build diff --git a/stac_model/input.py b/stac_model/input.py index b40a458..8e831db 100644 --- a/stac_model/input.py +++ b/stac_model/input.py @@ -28,7 +28,8 @@ class MLMStatistic(MLMBaseModel): # FIXME: add 'Statistics' dep from raster ext valid_percent: Annotated[Number | None, OmitIfNone] = None -NormalizeType: TypeAlias = Literal[ +NormalizeType: TypeAlias = ( + Literal[ "min-max", "z-score", "l1", @@ -39,9 +40,12 @@ class MLMStatistic(MLMBaseModel): # FIXME: add 'Statistics' dep from raster ext "type-mask", "relative", "inf", - ] | None # noqa: E501 + ] + | None +) # noqa: E501 -ResizeType: TypeAlias = Literal[ +ResizeType: TypeAlias = ( + Literal[ "crop", "pad", "interpolation-nearest", @@ -52,7 +56,9 @@ class MLMStatistic(MLMBaseModel): # FIXME: add 'Statistics' dep from raster ext "interpolation-max", "wrap-fill-outliers", "wrap-inverse-map", - ] | None # noqa: E501 + ] + | None +) # noqa: E501 class ModelBand(MLMBaseModel): diff --git a/stac_model/schema.py b/stac_model/schema.py index aea1e41..e2d2b5a 100644 --- a/stac_model/schema.py +++ b/stac_model/schema.py @@ -94,18 +94,15 @@ def get_schema_uri(cls) -> str: @overload @classmethod - def ext(cls, obj: pystac.Asset, add_if_missing: bool = False) -> "AssetMLModelExtension": - ... + def ext(cls, obj: pystac.Asset, add_if_missing: bool = False) -> "AssetMLModelExtension": ... @overload @classmethod - def ext(cls, obj: pystac.Item, add_if_missing: bool = False) -> "ItemMLModelExtension": - ... + def ext(cls, obj: pystac.Item, add_if_missing: bool = False) -> "ItemMLModelExtension": ... @overload @classmethod - def ext(cls, obj: pystac.Collection, add_if_missing: bool = False) -> "CollectionMLModelExtension": - ... + def ext(cls, obj: pystac.Collection, add_if_missing: bool = False) -> "CollectionMLModelExtension": ... # @overload # @classmethod From 277b1d7bbb458cacb6e324cb85e0cd818ecda3b8 Mon Sep 17 00:00:00 2001 From: Ryan Avery Date: Tue, 5 Nov 2024 10:30:12 -0800 Subject: [PATCH 40/83] fix if condition on linux, linitng --- Makefile | 7 ++++--- tests/test_schema.py | 2 +- 2 files changed, 5 insertions(+), 4 deletions(-) diff --git a/Makefile b/Makefile index 4e51def..2240e8f 100644 --- a/Makefile +++ b/Makefile @@ -5,10 +5,11 @@ ACTIVEPYTHON = $(shell which python) #* UV .PHONY: setup setup: - @if ! command -v uv &> /dev/null; then \ - curl -LsSf https://astral.sh/uv/install.sh | sh; \ - else \ + @if command -v uv &> /dev/null; then \ echo "uv is already installed"; \ + else \ + echo "uv not found, installing..."; \ + curl -LsSf https://astral.sh/uv/install.sh | sh; \ fi .PHONY: publish diff --git a/tests/test_schema.py b/tests/test_schema.py index 7864f14..0f949b2 100644 --- a/tests/test_schema.py +++ b/tests/test_schema.py @@ -34,7 +34,7 @@ def test_mlm_schema( ) def test_mlm_no_undefined_prefixed_field( mlm_validator: STACValidator, - mlm_example: Dict[str, JSON], + mlm_example: dict[str, JSON], ) -> None: mlm_data = copy.deepcopy(mlm_example) mlm_item = pystac.Item.from_dict(mlm_data) From a8cf7ca13adf0f0e29470596013e7fbc6fc52e38 Mon Sep 17 00:00:00 2001 From: Ryan Avery Date: Tue, 5 Nov 2024 10:40:47 -0800 Subject: [PATCH 41/83] don't run setup on every command in makefile --- Makefile | 27 +++++++++++---------------- tests/test_schema.py | 5 +---- 2 files changed, 12 insertions(+), 20 deletions(-) diff --git a/Makefile b/Makefile index 2240e8f..f7733f9 100644 --- a/Makefile +++ b/Makefile @@ -5,12 +5,7 @@ ACTIVEPYTHON = $(shell which python) #* UV .PHONY: setup setup: - @if command -v uv &> /dev/null; then \ - echo "uv is already installed"; \ - else \ - echo "uv not found, installing..."; \ - curl -LsSf https://astral.sh/uv/install.sh | sh; \ - fi + curl -LsSf https://astral.sh/uv/install.sh | sh .PHONY: publish publish: @@ -18,22 +13,22 @@ publish: #* Installation .PHONY: install -install: setup +install: uv export --format requirements-txt -o requirements.txt --no-dev uv pip install --python $(ACTIVEPYTHON) -r requirements.txt .PHONY: install-dev -install-dev: setup +install-dev: uv export --format requirements-txt -o requirements-dev.txt uv pip install --python $(ACTIVEPYTHON) -r requirements-dev.txt .PHONY: pre-commit-install -pre-commit-install: setup +pre-commit-install: uv run --python $(ACTIVEPYTHON) pre-commit install #* Formatters .PHONY: codestyle -codestyle: setup +codestyle: uv run --python $(ACTIVEPYTHON) ruff format --config=pyproject.toml stac_model tests .PHONY: format @@ -41,7 +36,7 @@ format: codestyle #* Linting .PHONY: test -test: setup +test: uv run --python $(ACTIVEPYTHON) pytest -c pyproject.toml --cov-report=html --cov=stac_model tests/ .PHONY: check @@ -51,19 +46,19 @@ check: check-examples check-markdown check-lint check-mypy check-safety check-ci check-all: check .PHONY: mypy -mypy: setup +mypy: uv run --python $(ACTIVEPYTHON) mypy --config-file pyproject.toml ./ .PHONY: check-mypy check-mypy: mypy .PHONY: check-safety -check-safety: setup +check-safety: uv run --python $(ACTIVEPYTHON) safety check --full-report uv run --python $(ACTIVEPYTHON) bandit -ll --recursive stac_model tests .PHONY: lint -lint: setup +lint: uv run --python $(ACTIVEPYTHON) ruff check --fix --config=pyproject.toml ./ .PHONY: check-lint @@ -71,7 +66,7 @@ check-lint: lint uv run --python $(ACTIVEPYTHON) ruff check --config=pyproject.toml ./ .PHONY: format-lint -format-lint: setup lint +format-lint: lint ruff format --config=pyproject.toml ./ .PHONY: install-npm @@ -101,7 +96,7 @@ $(addprefix fix-, $(FORMATTERS)): fix-%: format-% lint-all: lint mypy check-safety check-markdown .PHONY: update-dev-deps -update-dev-deps: setup +update-dev-deps: uv export --only-dev --format requirements-txt -o requirements-only-dev.txt uv pip install --python $(ACTIVEPYTHON) -r requirements-only-dev.txt diff --git a/tests/test_schema.py b/tests/test_schema.py index 0f949b2..6825a51 100644 --- a/tests/test_schema.py +++ b/tests/test_schema.py @@ -44,10 +44,7 @@ def test_mlm_no_undefined_prefixed_field( with pytest.raises(pystac.errors.STACValidationError) as exc: mlm_item = pystac.Item.from_dict(mlm_data) pystac.validation.validate(mlm_item, validator=mlm_validator) - assert all( - field in str(exc.value.source) - for field in ["mlm:unknown", "^(?!mlm:)"] - ) + assert all(field in str(exc.value.source) for field in ["mlm:unknown", "^(?!mlm:)"]) @pytest.mark.parametrize( From 2a5963d4f01d3f8e614e7c4d7098f81d0c018553 Mon Sep 17 00:00:00 2001 From: Ryan Avery Date: Tue, 5 Nov 2024 10:44:37 -0800 Subject: [PATCH 42/83] disable dependabot --- .github/dependabot.yml | 44 +++++++++++++++++++++--------------------- 1 file changed, 22 insertions(+), 22 deletions(-) diff --git a/.github/dependabot.yml b/.github/dependabot.yml index ff92d7a..6cce39a 100644 --- a/.github/dependabot.yml +++ b/.github/dependabot.yml @@ -1,25 +1,25 @@ -# Configuration: https://dependabot.com/docs/config-file/ -# Docs: https://docs.github.com/en/github/administering-a-repository/keeping-your-dependencies-updated-automatically +# # Configuration: https://dependabot.com/docs/config-file/ +# # Docs: https://docs.github.com/en/github/administering-a-repository/keeping-your-dependencies-updated-automatically -version: 2 +# version: 2 -updates: - - package-ecosystem: "pip" - directory: "/" - schedule: - interval: "monthly" - allow: - - dependency-type: "all" - commit-message: - prefix: ":arrow_up:" - open-pull-requests-limit: 5 +# updates: +# - package-ecosystem: "pip" +# directory: "/" +# schedule: +# interval: "monthly" +# allow: +# - dependency-type: "all" +# commit-message: +# prefix: ":arrow_up:" +# open-pull-requests-limit: 5 - - package-ecosystem: "github-actions" - directory: "/" - schedule: - interval: "monthly" - allow: - - dependency-type: "all" - commit-message: - prefix: ":arrow_up:" - open-pull-requests-limit: 5 +# - package-ecosystem: "github-actions" +# directory: "/" +# schedule: +# interval: "monthly" +# allow: +# - dependency-type: "all" +# commit-message: +# prefix: ":arrow_up:" +# open-pull-requests-limit: 5 From e15f0adbb0defa3a1aa056015c9cefde28caf2ae Mon Sep 17 00:00:00 2001 From: Ryan Avery Date: Tue, 5 Nov 2024 10:45:23 -0800 Subject: [PATCH 43/83] temporarily remove dependabot --- .github/dependabot.yml | 25 ------------------------- 1 file changed, 25 deletions(-) delete mode 100644 .github/dependabot.yml diff --git a/.github/dependabot.yml b/.github/dependabot.yml deleted file mode 100644 index 6cce39a..0000000 --- a/.github/dependabot.yml +++ /dev/null @@ -1,25 +0,0 @@ -# # Configuration: https://dependabot.com/docs/config-file/ -# # Docs: https://docs.github.com/en/github/administering-a-repository/keeping-your-dependencies-updated-automatically - -# version: 2 - -# updates: -# - package-ecosystem: "pip" -# directory: "/" -# schedule: -# interval: "monthly" -# allow: -# - dependency-type: "all" -# commit-message: -# prefix: ":arrow_up:" -# open-pull-requests-limit: 5 - -# - package-ecosystem: "github-actions" -# directory: "/" -# schedule: -# interval: "monthly" -# allow: -# - dependency-type: "all" -# commit-message: -# prefix: ":arrow_up:" -# open-pull-requests-limit: 5 From b98a7c418006187f2be91f9a6496db61b9fa7ea4 Mon Sep 17 00:00:00 2001 From: Francis Charette Migneault Date: Tue, 5 Nov 2024 21:43:20 -0500 Subject: [PATCH 44/83] more strict validation of MLM-prefixed fields under Assets and Item properties --- CHANGELOG.md | 1 + json-schema/schema.json | 91 +++++++++++++++++++++++++++++++---------- tests/test_schema.py | 76 +++++++++++++++++++++++++++++++++- 3 files changed, 144 insertions(+), 24 deletions(-) diff --git a/CHANGELOG.md b/CHANGELOG.md index 5dc5e17..ddd646e 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -16,6 +16,7 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0 for the entire input (but still possible). ### Changed +- Use JSON `$schema` version `2019-09` to allow use of `unevaluatedProperties` for stricter validation of MLM fields. - Moved `norm_type` to `value_scaling` object to better reflect the expected operation, which could be another operation than what is typically known as "normalization" or "standardization" techniques in machine learning. - Moved `statistics` to `value_scaling` object to better reflect their mutual `type` and additional diff --git a/json-schema/schema.json b/json-schema/schema.json index 3fe74c4..42371c9 100644 --- a/json-schema/schema.json +++ b/json-schema/schema.json @@ -1,5 +1,5 @@ { - "$schema": "http://json-schema.org/draft-07/schema#", + "$schema": "https://json-schema.org/draft/2019-09/schema#", "$id": "https://stac-extensions.github.io/mlm/v1.3.0/schema.json", "title": "Machine Learning Model STAC Extension Schema", "description": "This object represents the metadata for a Machine Learning Model (MLM) used in STAC documents.", @@ -20,7 +20,7 @@ "then": { "allOf": [ { - "$comment": "Schema to validate the MLM fields under Item properties or Assets properties.", + "description": "Schema to validate the MLM fields permitted under Item properties or Assets properties.", "type": "object", "required": [ "properties", @@ -28,8 +28,11 @@ ], "properties": { "properties": { + "description": "Schema to validate the MLM fields permitted under Item properties.", + "$comment": "Allow properties not defined by MLM prefix to work with other extensions and attributes, but disallow undefined MLM fields.", "allOf": [ { + "type": "object", "required": [ "mlm:name", "mlm:architecture", @@ -39,18 +42,26 @@ ] }, { - "$ref": "#/$defs/fields" + "$ref": "#/$defs/mlmItemFields" + }, + { + "patternProperties": { + "^(?!mlm:)": {} + } } - ] + ], + "unevaluatedProperties": false }, "assets": { "type": "object", "additionalProperties": { - "allOf": [ - { - "$ref": "#/$defs/fields" - } - ] + "description": "Schema to validate the MLM fields permitted only under Assets properties.", + "$comment": "Allow properties not defined by MLM prefix to work with other extensions and attributes, but disallow undefined MLM fields.", + "$ref": "#/$defs/mlmAssetFields", + "patternProperties": { + "^(?!mlm:)": {} + }, + "unevaluatedProperties": false } } } @@ -93,19 +104,19 @@ "summaries": { "type": "object", "additionalProperties": { - "$ref": "#/$defs/fields" + "$ref": "#/$defs/mlmCollectionFields" } }, "assets": { "type": "object", "additionalProperties": { - "$ref": "#/$defs/fields" + "$ref": "#/$defs/mlmAssetFields" } }, "item_assets": { "type": "object", "additionalProperties": { - "$ref": "#/$defs/fields" + "$ref": "#/$defs/mlmAssetFields" } } } @@ -261,12 +272,10 @@ } } }, - "fields": { + "mlmSharedFields": { + "description": "MLM fields that apply at any level (Collection, Item, Asset, Link).", "type": "object", "properties": { - "mlm:name": { - "$ref": "#/$defs/mlm:name" - }, "mlm:architecture": { "$ref": "#/$defs/mlm:architecture" }, @@ -305,6 +314,30 @@ }, "mlm:accelerator_count": { "$ref": "#/$defs/mlm:accelerator_count" + } + } + }, + "mlmCollectionFields": { + "$ref": "#/$defs/mlmSharedFields" + }, + "mlmItemFields": { + "description": "MLM fields that apply at any level (Collection, Item, Asset, Link).", + "type": "object", + "allOf": [ + { + "$ref": "#/$defs/mlmItemOnlyFields" + }, + { + "$ref": "#/$defs/mlmSharedFields" + } + ] + }, + "mlmItemOnlyFields": { + "description": "MLM fields that apply at any level (Collection, Item, Asset, Link).", + "type": "object", + "properties": { + "mlm:name": { + "$ref": "#/$defs/mlm:name" }, "mlm:input": { "$ref": "#/$defs/mlm:input" @@ -315,12 +348,26 @@ "mlm:hyperparameters": { "$ref": "#/$defs/mlm:hyperparameters" } - }, - "$comment": "Allow properties not defined by MLM prefix to allow combination with other extensions.", - "patternProperties": { - "^(?!mlm:)": {} - }, - "additionalProperties": false + } + }, + "mlmAssetFields": { + "allOf": [ + { + "$ref": "#/$defs/mlmSharedFields" + }, + { + "$ref": "#/$defs/mlmAssetOnlyFields" + } + ] + }, + "mlmAssetOnlyFields": { + "description": "MLM fields that apply only within an Asset.", + "type": "object", + "properties": { + "mlm:artifact_type": { + "$ref": "#/$defs/mlm:artifact_type" + } + } }, "mlm:name": { "type": "string", diff --git a/tests/test_schema.py b/tests/test_schema.py index 7035e8e..20441a7 100644 --- a/tests/test_schema.py +++ b/tests/test_schema.py @@ -32,7 +32,7 @@ def test_mlm_schema( ["item_raster_bands.json"], indirect=True, ) -def test_mlm_no_undefined_prefixed_field( +def test_mlm_no_undefined_prefixed_field_item_properties( mlm_validator: STACValidator, mlm_example: Dict[str, JSON], ) -> None: @@ -40,16 +40,62 @@ def test_mlm_no_undefined_prefixed_field( mlm_item = pystac.Item.from_dict(mlm_data) pystac.validation.validate(mlm_item, validator=mlm_validator) # ensure original is valid + # undefined property anywhere in the schema + mlm_data = copy.deepcopy(mlm_example) mlm_data["properties"]["mlm:unknown"] = "random" # type: ignore + with pytest.raises(pystac.errors.STACValidationError) as exc: + mlm_item = pystac.Item.from_dict(mlm_data) + pystac.validation.validate(mlm_item, validator=mlm_validator) + assert all( + info in str(exc.value.source) + for info in ["mlm:unknown", "Unevaluated properties are not allowed"] + ) + + # defined property only allowed at the Asset level + mlm_data = copy.deepcopy(mlm_example) + mlm_data["properties"]["mlm:artifact_type"] = "torch.save" with pytest.raises(pystac.errors.STACValidationError) as exc: mlm_item = pystac.Item.from_dict(mlm_data) pystac.validation.validate(mlm_item, validator=mlm_validator) assert all( field in str(exc.value.source) - for field in ["mlm:unknown", "^(?!mlm:)"] + for field in ["mlm:artifact_type", "Unevaluated properties are not allowed"] ) +@pytest.mark.parametrize( + "mlm_example", + ["item_raster_bands.json"], + indirect=True, +) +def test_mlm_no_undefined_prefixed_field_asset_properties( + mlm_validator: STACValidator, + mlm_example: Dict[str, JSON], +) -> None: + mlm_data = copy.deepcopy(mlm_example) + mlm_item = pystac.Item.from_dict(mlm_data) + pystac.validation.validate(mlm_item, validator=mlm_validator) # ensure original is valid + + assert mlm_data["assets"]["weights"] + mlm_data["assets"]["weights"]["mlm:unknown"] = "random" # type: ignore + with pytest.raises(pystac.errors.STACValidationError) as exc: + mlm_item = pystac.Item.from_dict(mlm_data) + pystac.validation.validate(mlm_item, validator=mlm_validator) + assert len(exc.value.source) == 1 + schema_error = exc.value.source[0] + assert "mlm:unknown" in schema_error.instance + assert schema_error.schema["description"] in [ + "Fields that apply only within an Asset.", + "Schema to validate the MLM fields permitted only under Assets properties." + ] + + # defined property allowed both at the Item at the Asset level + mlm_data = copy.deepcopy(mlm_example) + mlm_data["assets"]["weights"]["mlm:accelerator"] = "cuda" + mlm_item = pystac.Item.from_dict(mlm_data) + pystac.validation.validate(mlm_item, validator=mlm_validator) + + @pytest.mark.parametrize( "mlm_example", ["item_raster_bands.json"], @@ -224,6 +270,32 @@ def test_mlm_at_least_one_asset_model( ] +@pytest.mark.parametrize( + "mlm_example", + ["item_basic.json"], + indirect=True, +) +def test_mlm_asset_artifact_type_checked( + mlm_validator: STACValidator, + mlm_example: Dict[str, JSON], +) -> None: + mlm_data = copy.deepcopy(mlm_example) + mlm_item = pystac.Item.from_dict(mlm_data) + pystac.validation.validate(mlm_item, validator=mlm_validator) # self-check valid beforehand + + mlm_data["assets"]["model"]["mlm:artifact_type"] = 1234 + mlm_item = pystac.Item.from_dict(mlm_data) + with pytest.raises(pystac.errors.STACValidationError) as exc: + pystac.validation.validate(mlm_item, validator=mlm_validator) + assert "1234 is not of type 'string'" in str(exc.value.source) + + mlm_data["assets"]["model"]["mlm:artifact_type"] = "" + mlm_item = pystac.Item.from_dict(mlm_data) + with pytest.raises(pystac.errors.STACValidationError) as exc: + pystac.validation.validate(mlm_item, validator=mlm_validator) + assert "should be non-empty" in str(exc.value.source) + + def test_model_metadata_to_dict(eurosat_resnet): assert eurosat_resnet.item.to_dict() From 28c2983ac6eb19b8585e87eba5b83c36d1500b42 Mon Sep 17 00:00:00 2001 From: Francis Charette Migneault Date: Tue, 5 Nov 2024 21:51:32 -0500 Subject: [PATCH 45/83] ignore typing errors from mypy in tests --- tests/test_schema.py | 14 +++++++------- 1 file changed, 7 insertions(+), 7 deletions(-) diff --git a/tests/test_schema.py b/tests/test_schema.py index 20441a7..bda53d1 100644 --- a/tests/test_schema.py +++ b/tests/test_schema.py @@ -53,7 +53,7 @@ def test_mlm_no_undefined_prefixed_field_item_properties( # defined property only allowed at the Asset level mlm_data = copy.deepcopy(mlm_example) - mlm_data["properties"]["mlm:artifact_type"] = "torch.save" + mlm_data["properties"]["mlm:artifact_type"] = "torch.save" # type: ignore with pytest.raises(pystac.errors.STACValidationError) as exc: mlm_item = pystac.Item.from_dict(mlm_data) pystac.validation.validate(mlm_item, validator=mlm_validator) @@ -76,13 +76,13 @@ def test_mlm_no_undefined_prefixed_field_asset_properties( mlm_item = pystac.Item.from_dict(mlm_data) pystac.validation.validate(mlm_item, validator=mlm_validator) # ensure original is valid - assert mlm_data["assets"]["weights"] + assert mlm_data["assets"]["weights"] # type: ignore mlm_data["assets"]["weights"]["mlm:unknown"] = "random" # type: ignore with pytest.raises(pystac.errors.STACValidationError) as exc: mlm_item = pystac.Item.from_dict(mlm_data) pystac.validation.validate(mlm_item, validator=mlm_validator) - assert len(exc.value.source) == 1 - schema_error = exc.value.source[0] + assert len(exc.value.source) == 1 # type: ignore + schema_error = exc.value.source[0] # type: ignore assert "mlm:unknown" in schema_error.instance assert schema_error.schema["description"] in [ "Fields that apply only within an Asset.", @@ -91,7 +91,7 @@ def test_mlm_no_undefined_prefixed_field_asset_properties( # defined property allowed both at the Item at the Asset level mlm_data = copy.deepcopy(mlm_example) - mlm_data["assets"]["weights"]["mlm:accelerator"] = "cuda" + mlm_data["assets"]["weights"]["mlm:accelerator"] = "cuda" # type: ignore mlm_item = pystac.Item.from_dict(mlm_data) pystac.validation.validate(mlm_item, validator=mlm_validator) @@ -283,13 +283,13 @@ def test_mlm_asset_artifact_type_checked( mlm_item = pystac.Item.from_dict(mlm_data) pystac.validation.validate(mlm_item, validator=mlm_validator) # self-check valid beforehand - mlm_data["assets"]["model"]["mlm:artifact_type"] = 1234 + mlm_data["assets"]["model"]["mlm:artifact_type"] = 1234 # type: ignore mlm_item = pystac.Item.from_dict(mlm_data) with pytest.raises(pystac.errors.STACValidationError) as exc: pystac.validation.validate(mlm_item, validator=mlm_validator) assert "1234 is not of type 'string'" in str(exc.value.source) - mlm_data["assets"]["model"]["mlm:artifact_type"] = "" + mlm_data["assets"]["model"]["mlm:artifact_type"] = "" # type: ignore mlm_item = pystac.Item.from_dict(mlm_data) with pytest.raises(pystac.errors.STACValidationError) as exc: pystac.validation.validate(mlm_item, validator=mlm_validator) From c80a5e624aac0c531c574cee05e87b81de5aeb93 Mon Sep 17 00:00:00 2001 From: Francis Charette Migneault Date: Tue, 5 Nov 2024 22:42:17 -0500 Subject: [PATCH 46/83] fix missing package/bump as stac-mlm --- package.json | 4 ++-- pyproject.toml | 5 +++++ 2 files changed, 7 insertions(+), 2 deletions(-) diff --git a/package.json b/package.json index 13d0079..b6cbbdd 100644 --- a/package.json +++ b/package.json @@ -1,6 +1,6 @@ { - "name": "stac-extensions", - "version": "1.0.0", + "name": "stac-mlm", + "version": "1.3.0", "scripts": { "test": "npm run check-markdown && npm run check-examples", "check-markdown": "remark . -f -r .github/remark.yaml -i .remarkignore", diff --git a/pyproject.toml b/pyproject.toml index f76d9fe..19d507b 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -191,6 +191,11 @@ filename = "CITATION.cff" search = "https://stac-extensions.github.io/mlm/v{current_version}/schema.json" replace = "https://stac-extensions.github.io/mlm/v{new_version}/schema.json" +[[tool.bumpversion.files]] +filename = "package.json" +search = "\"version\": \"{current_version}\"" +replace = "\"version\": \"{new_version}\"" + [tool.ruff] exclude = [ ".git", From 1f53bfac5144ce71f12ba6d43bbdab3b5de90488 Mon Sep 17 00:00:00 2001 From: Francis Charette Migneault Date: Wed, 6 Nov 2024 13:45:58 -0500 Subject: [PATCH 47/83] add more tests to validate MLM fields disallowed under Asset --- CHANGELOG.md | 2 ++ tests/test_schema.py | 29 ++++++++++++++++++++++++++--- 2 files changed, 28 insertions(+), 3 deletions(-) diff --git a/CHANGELOG.md b/CHANGELOG.md index ddd646e..2726743 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -17,6 +17,8 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0 ### Changed - Use JSON `$schema` version `2019-09` to allow use of `unevaluatedProperties` for stricter validation of MLM fields. +- Explicitly disallow `mlm:name`, `mlm:input`, `mlm:output` and `mlm:hyperparameters` at the Asset level. + These fields describe the model as a whole and should therefore be defined in Item properties. - Moved `norm_type` to `value_scaling` object to better reflect the expected operation, which could be another operation than what is typically known as "normalization" or "standardization" techniques in machine learning. - Moved `statistics` to `value_scaling` object to better reflect their mutual `type` and additional diff --git a/tests/test_schema.py b/tests/test_schema.py index bda53d1..d3152a8 100644 --- a/tests/test_schema.py +++ b/tests/test_schema.py @@ -68,27 +68,50 @@ def test_mlm_no_undefined_prefixed_field_item_properties( ["item_raster_bands.json"], indirect=True, ) +@pytest.mark.parametrize( + ["test_field", "test_value"], + [ + ("mlm:unknown", "random"), + ("mlm:name", "test-model"), + ("mlm:input", []), + ("mlm:output", []), + ("mlm:hyperparameters", {}), + ] +) def test_mlm_no_undefined_prefixed_field_asset_properties( mlm_validator: STACValidator, mlm_example: Dict[str, JSON], + test_field: str, + test_value: Any, ) -> None: mlm_data = copy.deepcopy(mlm_example) mlm_item = pystac.Item.from_dict(mlm_data) pystac.validation.validate(mlm_item, validator=mlm_validator) # ensure original is valid - assert mlm_data["assets"]["weights"] # type: ignore - mlm_data["assets"]["weights"]["mlm:unknown"] = "random" # type: ignore + + mlm_data = copy.deepcopy(mlm_example) + mlm_data["assets"]["weights"][test_field] = test_value # type: ignore with pytest.raises(pystac.errors.STACValidationError) as exc: mlm_item = pystac.Item.from_dict(mlm_data) pystac.validation.validate(mlm_item, validator=mlm_validator) assert len(exc.value.source) == 1 # type: ignore schema_error = exc.value.source[0] # type: ignore - assert "mlm:unknown" in schema_error.instance + assert test_field in schema_error.instance assert schema_error.schema["description"] in [ "Fields that apply only within an Asset.", "Schema to validate the MLM fields permitted only under Assets properties." ] + +@pytest.mark.parametrize( + "mlm_example", + ["item_raster_bands.json"], + indirect=True, +) +def test_mlm_allowed_field_asset_properties_override( + mlm_validator: STACValidator, + mlm_example: Dict[str, JSON], +) -> None: # defined property allowed both at the Item at the Asset level mlm_data = copy.deepcopy(mlm_example) mlm_data["assets"]["weights"]["mlm:accelerator"] = "cuda" # type: ignore From 007176a785625bd5a9423dffb0aeeb2a161a55df Mon Sep 17 00:00:00 2001 From: Francis Charette Migneault Date: Wed, 6 Nov 2024 14:04:51 -0500 Subject: [PATCH 48/83] add more explicit indication about Item-only vs shared Item/Asset MLM fields --- README.md | 63 ++++++++++++++++++++++++++++++------------------------- 1 file changed, 35 insertions(+), 28 deletions(-) diff --git a/README.md b/README.md index f427c99..1e82e73 100644 --- a/README.md +++ b/README.md @@ -116,34 +116,41 @@ The fields in the table below can be used in these parts of STAC documents: [item-assets]: https://github.com/stac-extensions/item-assets -| Field Name | Type | Description | -|-----------------------------|---------------------------------------------------------------|---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| -| mlm:name | string | **REQUIRED** A name for the model. This can include, but must be distinct, from simply naming the model architecture. If there is a publication or other published work related to the model, use the official name of the model. | -| mlm:architecture | [Model Architecture](#model-architecture) string | **REQUIRED** A generic and well established architecture name of the model. | -| mlm:tasks | \[[Task Enum](#task-enum)] | **REQUIRED** Specifies the Machine Learning tasks for which the model can be used for. If multi-tasks outputs are provided by distinct model heads, specify all available tasks under the main properties and specify respective tasks in each [Model Output Object](#model-output-object). | -| mlm:framework | string | Framework used to train the model (ex: PyTorch, TensorFlow). | -| mlm:framework_version | string | The `framework` library version. Some models require a specific version of the machine learning `framework` to run. | -| mlm:memory_size | integer | The in-memory size of the model on the accelerator during inference (bytes). | -| mlm:total_parameters | integer | Total number of model parameters, including trainable and non-trainable parameters. | -| mlm:pretrained | boolean | Indicates if the model was pretrained. If the model was pretrained, consider providing `pretrained_source` if it is known. | -| mlm:pretrained_source | string \| null | The source of the pretraining. Can refer to popular pretraining datasets by name (i.e. Imagenet) or less known datasets by URL and description. If trained from scratch (i.e.: `pretrained = false`), the `null` value should be set explicitly. | -| mlm:batch_size_suggestion | integer | A suggested batch size for the accelerator and summarized hardware. | -| mlm:accelerator | [Accelerator Type Enum](#accelerator-type-enum) \| null | The intended computational hardware that runs inference. If undefined or set to `null` explicitly, the model does not require any specific accelerator. | -| mlm:accelerator_constrained | boolean | Indicates if the intended `accelerator` is the only `accelerator` that can run inference. If undefined, it should be assumed `false`. | -| mlm:accelerator_summary | string | A high level description of the `accelerator`, such as its specific generation, or other relevant inference details. | -| mlm:accelerator_count | integer | A minimum amount of `accelerator` instances required to run the model. | -| mlm:input | \[[Model Input Object](#model-input-object)] | **REQUIRED** Describes the transformation between the EO data and the model input. | -| mlm:output | \[[Model Output Object](#model-output-object)] | **REQUIRED** Describes each model output and how to interpret it. | -| mlm:hyperparameters | [Model Hyperparameters Object](#model-hyperparameters-object) | Additional hyperparameters relevant for the model. | - -To decide whether above fields should be applied under Item `properties` or under respective Assets, the context of -each field must be considered. For example, the `mlm:name` should always be provided in the Item `properties`, since -it relates to the model as a whole. In contrast, some models could support multiple `mlm:accelerator`, which could be -handled by distinct source code represented by different Assets. In such case, `mlm:accelerator` definitions should be -nested under their relevant Asset. If a field is defined both at the Item and Asset level, the value at the Asset level -would be considered for that specific Asset, and the value at the Item level would be used for other Assets that did -not override it for their respective reference. For some of the fields, further details are provided in following -sections to provide more precisions regarding some potentially ambiguous use cases. +| Field Name | Type | Description | +|-----------------------------------------|---------------------------------------------------------------|---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| +| mlm:name [[1]][1] | string | **REQUIRED** A name for the model. This can include, but must be distinct, from simply naming the model architecture. If there is a publication or other published work related to the model, use the official name of the model. | +| mlm:architecture | [Model Architecture](#model-architecture) string | **REQUIRED** A generic and well established architecture name of the model. | +| mlm:tasks | \[[Task Enum](#task-enum)] | **REQUIRED** Specifies the Machine Learning tasks for which the model can be used for. If multi-tasks outputs are provided by distinct model heads, specify all available tasks under the main properties and specify respective tasks in each [Model Output Object](#model-output-object). | +| mlm:framework | string | Framework used to train the model (ex: PyTorch, TensorFlow). | +| mlm:framework_version | string | The `framework` library version. Some models require a specific version of the machine learning `framework` to run. | +| mlm:memory_size | integer | The in-memory size of the model on the accelerator during inference (bytes). | +| mlm:total_parameters | integer | Total number of model parameters, including trainable and non-trainable parameters. | +| mlm:pretrained | boolean | Indicates if the model was pretrained. If the model was pretrained, consider providing `pretrained_source` if it is known. | +| mlm:pretrained_source | string \| null | The source of the pretraining. Can refer to popular pretraining datasets by name (i.e. Imagenet) or less known datasets by URL and description. If trained from scratch (i.e.: `pretrained = false`), the `null` value should be set explicitly. | +| mlm:batch_size_suggestion | integer | A suggested batch size for the accelerator and summarized hardware. | +| mlm:accelerator | [Accelerator Type Enum](#accelerator-type-enum) \| null | The intended computational hardware that runs inference. If undefined or set to `null` explicitly, the model does not require any specific accelerator. | +| mlm:accelerator_constrained | boolean | Indicates if the intended `accelerator` is the only `accelerator` that can run inference. If undefined, it should be assumed `false`. | +| mlm:accelerator_summary | string | A high level description of the `accelerator`, such as its specific generation, or other relevant inference details. | +| mlm:accelerator_count | integer | A minimum amount of `accelerator` instances required to run the model. | +| mlm:input [[1]][1] | \[[Model Input Object](#model-input-object)] | **REQUIRED** Describes the transformation between the EO data and the model input. | +| mlm:output [[1]][1] | \[[Model Output Object](#model-output-object)] | **REQUIRED** Describes each model output and how to interpret it. | +| mlm:hyperparameters [[1]][1] | [Model Hyperparameters Object](#model-hyperparameters-object) | Additional hyperparameters relevant for the model. | + +[1]: #sup1sup-allowed-only-in-item-properties + +##### [1] Allowed Only in Item `properties` + +> [!NOTE] +> Unless stated otherwise by [[1]][1] in the table, fields can be used at either the Item or Asset level. +>

+> To decide whether above fields should be applied under Item `properties` or under respective Assets, the context of +> each field must be considered. For example, the `mlm:name` should always be provided in the Item `properties`, since +> it relates to the model as a whole. In contrast, some models could support multiple `mlm:accelerator`, which could be +> handled by distinct source code represented by different Assets. In such case, `mlm:accelerator` definitions should be +> nested under their relevant Asset. If a field is defined both at the Item and Asset level, the value at the Asset +> level would be considered for that specific Asset, and the value at the Item level would be used for other Assets that +> did not override it for their respective reference. For some of the fields, further details are provided in following +> sections to provide more precisions regarding some potentially ambiguous use cases. In addition, fields from the multiple relevant extensions should be defined as applicable. See [Best Practices - Recommended Extensions to Compose with the ML Model Extension](best-practices.md#recommended-extensions-to-compose-with-the-ml-model-extension) From 2b06e30f30318def095939f50307a93f2e9b7307 Mon Sep 17 00:00:00 2001 From: Francis Charette Migneault Date: Wed, 6 Nov 2024 14:08:45 -0500 Subject: [PATCH 49/83] fix brackets for markdown rendering --- README.md | 40 ++++++++++++++++++++-------------------- 1 file changed, 20 insertions(+), 20 deletions(-) diff --git a/README.md b/README.md index 1e82e73..9f1bc7d 100644 --- a/README.md +++ b/README.md @@ -116,32 +116,32 @@ The fields in the table below can be used in these parts of STAC documents: [item-assets]: https://github.com/stac-extensions/item-assets -| Field Name | Type | Description | -|-----------------------------------------|---------------------------------------------------------------|---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| -| mlm:name [[1]][1] | string | **REQUIRED** A name for the model. This can include, but must be distinct, from simply naming the model architecture. If there is a publication or other published work related to the model, use the official name of the model. | -| mlm:architecture | [Model Architecture](#model-architecture) string | **REQUIRED** A generic and well established architecture name of the model. | -| mlm:tasks | \[[Task Enum](#task-enum)] | **REQUIRED** Specifies the Machine Learning tasks for which the model can be used for. If multi-tasks outputs are provided by distinct model heads, specify all available tasks under the main properties and specify respective tasks in each [Model Output Object](#model-output-object). | -| mlm:framework | string | Framework used to train the model (ex: PyTorch, TensorFlow). | -| mlm:framework_version | string | The `framework` library version. Some models require a specific version of the machine learning `framework` to run. | -| mlm:memory_size | integer | The in-memory size of the model on the accelerator during inference (bytes). | -| mlm:total_parameters | integer | Total number of model parameters, including trainable and non-trainable parameters. | -| mlm:pretrained | boolean | Indicates if the model was pretrained. If the model was pretrained, consider providing `pretrained_source` if it is known. | -| mlm:pretrained_source | string \| null | The source of the pretraining. Can refer to popular pretraining datasets by name (i.e. Imagenet) or less known datasets by URL and description. If trained from scratch (i.e.: `pretrained = false`), the `null` value should be set explicitly. | -| mlm:batch_size_suggestion | integer | A suggested batch size for the accelerator and summarized hardware. | -| mlm:accelerator | [Accelerator Type Enum](#accelerator-type-enum) \| null | The intended computational hardware that runs inference. If undefined or set to `null` explicitly, the model does not require any specific accelerator. | -| mlm:accelerator_constrained | boolean | Indicates if the intended `accelerator` is the only `accelerator` that can run inference. If undefined, it should be assumed `false`. | -| mlm:accelerator_summary | string | A high level description of the `accelerator`, such as its specific generation, or other relevant inference details. | -| mlm:accelerator_count | integer | A minimum amount of `accelerator` instances required to run the model. | -| mlm:input [[1]][1] | \[[Model Input Object](#model-input-object)] | **REQUIRED** Describes the transformation between the EO data and the model input. | -| mlm:output [[1]][1] | \[[Model Output Object](#model-output-object)] | **REQUIRED** Describes each model output and how to interpret it. | -| mlm:hyperparameters [[1]][1] | [Model Hyperparameters Object](#model-hyperparameters-object) | Additional hyperparameters relevant for the model. | +| Field Name | Type | Description | +|-------------------------------------------|---------------------------------------------------------------|---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| +| mlm:name [\[1\]][1] | string | **REQUIRED** A name for the model. This can include, but must be distinct, from simply naming the model architecture. If there is a publication or other published work related to the model, use the official name of the model. | +| mlm:architecture | [Model Architecture](#model-architecture) string | **REQUIRED** A generic and well established architecture name of the model. | +| mlm:tasks | \[[Task Enum](#task-enum)] | **REQUIRED** Specifies the Machine Learning tasks for which the model can be used for. If multi-tasks outputs are provided by distinct model heads, specify all available tasks under the main properties and specify respective tasks in each [Model Output Object](#model-output-object). | +| mlm:framework | string | Framework used to train the model (ex: PyTorch, TensorFlow). | +| mlm:framework_version | string | The `framework` library version. Some models require a specific version of the machine learning `framework` to run. | +| mlm:memory_size | integer | The in-memory size of the model on the accelerator during inference (bytes). | +| mlm:total_parameters | integer | Total number of model parameters, including trainable and non-trainable parameters. | +| mlm:pretrained | boolean | Indicates if the model was pretrained. If the model was pretrained, consider providing `pretrained_source` if it is known. | +| mlm:pretrained_source | string \| null | The source of the pretraining. Can refer to popular pretraining datasets by name (i.e. Imagenet) or less known datasets by URL and description. If trained from scratch (i.e.: `pretrained = false`), the `null` value should be set explicitly. | +| mlm:batch_size_suggestion | integer | A suggested batch size for the accelerator and summarized hardware. | +| mlm:accelerator | [Accelerator Type Enum](#accelerator-type-enum) \| null | The intended computational hardware that runs inference. If undefined or set to `null` explicitly, the model does not require any specific accelerator. | +| mlm:accelerator_constrained | boolean | Indicates if the intended `accelerator` is the only `accelerator` that can run inference. If undefined, it should be assumed `false`. | +| mlm:accelerator_summary | string | A high level description of the `accelerator`, such as its specific generation, or other relevant inference details. | +| mlm:accelerator_count | integer | A minimum amount of `accelerator` instances required to run the model. | +| mlm:input [\[1\]][1] | \[[Model Input Object](#model-input-object)] | **REQUIRED** Describes the transformation between the EO data and the model input. | +| mlm:output [\[1\]][1] | \[[Model Output Object](#model-output-object)] | **REQUIRED** Describes each model output and how to interpret it. | +| mlm:hyperparameters [\[1\]][1] | [Model Hyperparameters Object](#model-hyperparameters-object) | Additional hyperparameters relevant for the model. | [1]: #sup1sup-allowed-only-in-item-properties ##### [1] Allowed Only in Item `properties` > [!NOTE] -> Unless stated otherwise by [[1]][1] in the table, fields can be used at either the Item or Asset level. +> Unless stated otherwise by [\[1\]][1] in the table, fields can be used at either the Item or Asset level. >

> To decide whether above fields should be applied under Item `properties` or under respective Assets, the context of > each field must be considered. For example, the `mlm:name` should always be provided in the Item `properties`, since From 66a9051c85776e7a49ed4a65dbaa25e928d7673b Mon Sep 17 00:00:00 2001 From: Francis Charette Migneault Date: Wed, 6 Nov 2024 14:04:51 -0500 Subject: [PATCH 50/83] add more explicit indication about Item-only vs shared Item/Asset MLM fields --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index 9f1bc7d..776268b 100644 --- a/README.md +++ b/README.md @@ -136,7 +136,7 @@ The fields in the table below can be used in these parts of STAC documents: | mlm:output [\[1\]][1] | \[[Model Output Object](#model-output-object)] | **REQUIRED** Describes each model output and how to interpret it. | | mlm:hyperparameters [\[1\]][1] | [Model Hyperparameters Object](#model-hyperparameters-object) | Additional hyperparameters relevant for the model. | -[1]: #sup1sup-allowed-only-in-item-properties +[1]: #1-allowed-only-in-item-properties ##### [1] Allowed Only in Item `properties` From e8f785b25ef116137f88c64598a40f40eaa3e8e4 Mon Sep 17 00:00:00 2001 From: Francis Charette Migneault Date: Wed, 6 Nov 2024 14:17:08 -0500 Subject: [PATCH 51/83] patch markdown linting of special header --- README.md | 7 +++++++ 1 file changed, 7 insertions(+) diff --git a/README.md b/README.md index 776268b..32689fa 100644 --- a/README.md +++ b/README.md @@ -136,6 +136,10 @@ The fields in the table below can be used in these parts of STAC documents: | mlm:output [\[1\]][1] | \[[Model Output Object](#model-output-object)] | **REQUIRED** Describes each model output and how to interpret it. | | mlm:hyperparameters [\[1\]][1] | [Model Hyperparameters Object](#model-hyperparameters-object) | Additional hyperparameters relevant for the model. | + + + + [1]: #1-allowed-only-in-item-properties ##### [1] Allowed Only in Item `properties` @@ -152,6 +156,9 @@ The fields in the table below can be used in these parts of STAC documents: > did not override it for their respective reference. For some of the fields, further details are provided in following > sections to provide more precisions regarding some potentially ambiguous use cases. + + + In addition, fields from the multiple relevant extensions should be defined as applicable. See [Best Practices - Recommended Extensions to Compose with the ML Model Extension](best-practices.md#recommended-extensions-to-compose-with-the-ml-model-extension) for more details. From abf4a5e180894f54a9312612e5f27f411593ea67 Mon Sep 17 00:00:00 2001 From: Francis Charette Migneault Date: Wed, 6 Nov 2024 15:32:39 -0500 Subject: [PATCH 52/83] patch markdown again --- README.md | 12 ++++++------ 1 file changed, 6 insertions(+), 6 deletions(-) diff --git a/README.md b/README.md index 32689fa..d3dff63 100644 --- a/README.md +++ b/README.md @@ -136,13 +136,14 @@ The fields in the table below can be used in these parts of STAC documents: | mlm:output [\[1\]][1] | \[[Model Output Object](#model-output-object)] | **REQUIRED** Describes each model output and how to interpret it. | | mlm:hyperparameters [\[1\]][1] | [Model Hyperparameters Object](#model-hyperparameters-object) | Additional hyperparameters relevant for the model. | - - - + -[1]: #1-allowed-only-in-item-properties +[1]: #_ -##### [1] Allowed Only in Item `properties` +### _ +[1][1] Fields allowed only in Item `properties` + + > [!NOTE] > Unless stated otherwise by [\[1\]][1] in the table, fields can be used at either the Item or Asset level. @@ -156,7 +157,6 @@ The fields in the table below can be used in these parts of STAC documents: > did not override it for their respective reference. For some of the fields, further details are provided in following > sections to provide more precisions regarding some potentially ambiguous use cases. - In addition, fields from the multiple relevant extensions should be defined as applicable. See From 778865a6ead6a5f00a724d6b2afa843c820f90b9 Mon Sep 17 00:00:00 2001 From: Francis Charette Migneault Date: Wed, 6 Nov 2024 15:37:30 -0500 Subject: [PATCH 53/83] use actual notes --- README.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/README.md b/README.md index d3dff63..9739397 100644 --- a/README.md +++ b/README.md @@ -138,9 +138,9 @@ The fields in the table below can be used in these parts of STAC documents: -[1]: #_ +[1]: #notes -### _ +### Notes [1][1] Fields allowed only in Item `properties` From 282fc4a6ff46e2b0d4b26ec160707a3fef78e532 Mon Sep 17 00:00:00 2001 From: Francis Charette Migneault Date: Wed, 6 Nov 2024 19:15:55 -0500 Subject: [PATCH 54/83] refactor the schema to replicate behavior of disallowed fields with JSON schema draft-07 --- CHANGELOG.md | 1 - json-schema/schema.json | 172 ++++++++++++++++++++++++---------------- tests/test_schema.py | 13 ++- 3 files changed, 108 insertions(+), 78 deletions(-) diff --git a/CHANGELOG.md b/CHANGELOG.md index 2726743..94afa3c 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -16,7 +16,6 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0 for the entire input (but still possible). ### Changed -- Use JSON `$schema` version `2019-09` to allow use of `unevaluatedProperties` for stricter validation of MLM fields. - Explicitly disallow `mlm:name`, `mlm:input`, `mlm:output` and `mlm:hyperparameters` at the Asset level. These fields describe the model as a whole and should therefore be defined in Item properties. - Moved `norm_type` to `value_scaling` object to better reflect the expected operation, which could be another diff --git a/json-schema/schema.json b/json-schema/schema.json index 42371c9..b87ad45 100644 --- a/json-schema/schema.json +++ b/json-schema/schema.json @@ -1,12 +1,12 @@ { - "$schema": "https://json-schema.org/draft/2019-09/schema#", + "$schema": "http://json-schema.org/draft-07/schema#", "$id": "https://stac-extensions.github.io/mlm/v1.3.0/schema.json", "title": "Machine Learning Model STAC Extension Schema", "description": "This object represents the metadata for a Machine Learning Model (MLM) used in STAC documents.", "$comment": "Use 'allOf+if/then' for each 'type' to allow implementations to report more specific messages about the exact case in error (if any). Using only a 'oneOf/allOf' with the 'type' caused any incompatible 'type' to be reported first with a minimal and poorly described error by 'pystac'.", "allOf": [ { - "$comment": "This is the schema for STAC extension MLM in Items.", + "description": "This is the schema for STAC extension MLM in Items.", "if": { "required": [ "type" @@ -28,40 +28,13 @@ ], "properties": { "properties": { - "description": "Schema to validate the MLM fields permitted under Item properties.", - "$comment": "Allow properties not defined by MLM prefix to work with other extensions and attributes, but disallow undefined MLM fields.", - "allOf": [ - { - "type": "object", - "required": [ - "mlm:name", - "mlm:architecture", - "mlm:tasks", - "mlm:input", - "mlm:output" - ] - }, - { - "$ref": "#/$defs/mlmItemFields" - }, - { - "patternProperties": { - "^(?!mlm:)": {} - } - } - ], - "unevaluatedProperties": false + "$comment": "Schema to validate the MLM fields permitted under Item properties.", + "$ref": "#/$defs/mlmItemFields" }, "assets": { - "type": "object", "additionalProperties": { - "description": "Schema to validate the MLM fields permitted only under Assets properties.", - "$comment": "Allow properties not defined by MLM prefix to work with other extensions and attributes, but disallow undefined MLM fields.", - "$ref": "#/$defs/mlmAssetFields", - "patternProperties": { - "^(?!mlm:)": {} - }, - "unevaluatedProperties": false + "$comment": "Schema to validate the MLM fields permitted under Asset properties.", + "$ref": "#/$defs/mlmAssetFields" } } } @@ -85,7 +58,7 @@ } }, { - "$comment": "This is the schema for STAC extension MLM in Collections.", + "description": "This is the schema for STAC extension MLM in Collections.", "if": { "required": [ "type" @@ -272,10 +245,13 @@ } } }, - "mlmSharedFields": { - "description": "MLM fields that apply at any level (Collection, Item, Asset, Link).", + "fields": { + "description": "All possible MLM fields regardless of the level they apply (Collection, Item, Asset, Link).", "type": "object", "properties": { + "mlm:name": { + "$ref": "#/$defs/mlm:name" + }, "mlm:architecture": { "$ref": "#/$defs/mlm:architecture" }, @@ -314,61 +290,119 @@ }, "mlm:accelerator_count": { "$ref": "#/$defs/mlm:accelerator_count" + }, + "mlm:input": { + "$ref": "#/$defs/mlm:input" + }, + "mlm:output": { + "$ref": "#/$defs/mlm:output" + }, + "mlm:hyperparameters": { + "$ref": "#/$defs/mlm:hyperparameters" + }, + "mlm:artifact_type": { + "$ref": "#/$defs/mlm:artifact_type" } - } + }, + "$comment": "Allow properties not defined by MLM prefix to work with other extensions and attributes, but disallow undefined MLM fields.", + "patternProperties": { + "^(?!mlm:)": {} + }, + "additionalProperties": false }, "mlmCollectionFields": { - "$ref": "#/$defs/mlmSharedFields" - }, - "mlmItemFields": { - "description": "MLM fields that apply at any level (Collection, Item, Asset, Link).", - "type": "object", + "description": "Schema to validate the MLM fields permitted under Collection summaries.", "allOf": [ { - "$ref": "#/$defs/mlmItemOnlyFields" + "description": "Fields that are mandatory under the Collection summaries.", + "type": "object", + "required": [] }, { - "$ref": "#/$defs/mlmSharedFields" + "description": "Fields that are disallowed under the Collection summaries.", + "not": { + "required": [ + "mlm:input", + "mlm:output", + "mlm:artifact_type" + ] + } + }, + { + "description": "Field with known definitions that must be validated.", + "$ref": "#/$defs/fields" } ] }, - "mlmItemOnlyFields": { - "description": "MLM fields that apply at any level (Collection, Item, Asset, Link).", - "type": "object", - "properties": { - "mlm:name": { - "$ref": "#/$defs/mlm:name" - }, - "mlm:input": { - "$ref": "#/$defs/mlm:input" + "mlmItemFields": { + "description": "Schema to validate the MLM fields permitted under Item properties.", + "allOf": [ + { + "description": "Fields that are mandatory under the Item properties.", + "required": [ + "mlm:name", + "mlm:architecture", + "mlm:tasks", + "mlm:input", + "mlm:output" + ] }, - "mlm:output": { - "$ref": "#/$defs/mlm:output" + { + "description": "Fields that are disallowed under the Item properties.", + "$comment": "Particularity of the 'not/required' approach: they must be tested one by one. Otherwise, it validates that they are all (simultaneously) not present.", + "not": { + "anyOf": [ + { + "required": [ + "mlm:artifact_type" + ] + } + ] + } }, - "mlm:hyperparameters": { - "$ref": "#/$defs/mlm:hyperparameters" + { + "description": "Field with known definitions that must be validated.", + "$ref": "#/$defs/fields" } - } + ] }, "mlmAssetFields": { + "description": "Schema to validate the MLM fields permitted under Assets properties.", "allOf": [ { - "$ref": "#/$defs/mlmSharedFields" + "description": "Fields that are disallowed under the Asset properties.", + "$comment": "Particularity of the 'not/required' approach: they must be tested one by one. Otherwise, it validates that they are all (simultaneously) not present.", + "not": { + "anyOf": [ + { + "required": [ + "mlm:name" + ] + }, + { + "required": [ + "mlm:input" + ] + }, + { + "required": [ + "mlm:output" + ] + }, + { + "required": [ + "mlm:hyperparameters" + ] + } + ] + } }, { - "$ref": "#/$defs/mlmAssetOnlyFields" + "description": "Field with known definitions that must be validated.", + "$ref": "#/$defs/fields" } ] }, - "mlmAssetOnlyFields": { - "description": "MLM fields that apply only within an Asset.", - "type": "object", - "properties": { - "mlm:artifact_type": { - "$ref": "#/$defs/mlm:artifact_type" - } - } - }, "mlm:name": { "type": "string", "pattern": "^[a-zA-Z][a-zA-Z0-9_.\\-\\s]+[a-zA-Z0-9]$" diff --git a/tests/test_schema.py b/tests/test_schema.py index d3152a8..633b087 100644 --- a/tests/test_schema.py +++ b/tests/test_schema.py @@ -48,7 +48,7 @@ def test_mlm_no_undefined_prefixed_field_item_properties( pystac.validation.validate(mlm_item, validator=mlm_validator) assert all( info in str(exc.value.source) - for info in ["mlm:unknown", "Unevaluated properties are not allowed"] + for info in ["mlm:unknown", "^(?!mlm:)"] ) # defined property only allowed at the Asset level @@ -57,10 +57,7 @@ def test_mlm_no_undefined_prefixed_field_item_properties( with pytest.raises(pystac.errors.STACValidationError) as exc: mlm_item = pystac.Item.from_dict(mlm_data) pystac.validation.validate(mlm_item, validator=mlm_validator) - assert all( - field in str(exc.value.source) - for field in ["mlm:artifact_type", "Unevaluated properties are not allowed"] - ) + assert exc.value.source[0].validator_value == {"required": ["mlm:artifact_type"]} @pytest.mark.parametrize( @@ -75,7 +72,7 @@ def test_mlm_no_undefined_prefixed_field_item_properties( ("mlm:name", "test-model"), ("mlm:input", []), ("mlm:output", []), - ("mlm:hyperparameters", {}), + ("mlm:hyperparameters", {"test": {}}), ] ) def test_mlm_no_undefined_prefixed_field_asset_properties( @@ -98,8 +95,8 @@ def test_mlm_no_undefined_prefixed_field_asset_properties( schema_error = exc.value.source[0] # type: ignore assert test_field in schema_error.instance assert schema_error.schema["description"] in [ - "Fields that apply only within an Asset.", - "Schema to validate the MLM fields permitted only under Assets properties." + "All possible MLM fields regardless of the level they apply (Collection, Item, Asset, Link).", + "Fields that are disallowed under the Asset properties." ] From bcfb82ab68b39d29bbffa9053a2e7053e6733f10 Mon Sep 17 00:00:00 2001 From: Francis Charette Migneault Date: Wed, 6 Nov 2024 19:24:01 -0500 Subject: [PATCH 55/83] adjust test asset to be more adaptable to future schema changes --- tests/test_schema.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/tests/test_schema.py b/tests/test_schema.py index 633b087..f3bb313 100644 --- a/tests/test_schema.py +++ b/tests/test_schema.py @@ -57,7 +57,8 @@ def test_mlm_no_undefined_prefixed_field_item_properties( with pytest.raises(pystac.errors.STACValidationError) as exc: mlm_item = pystac.Item.from_dict(mlm_data) pystac.validation.validate(mlm_item, validator=mlm_validator) - assert exc.value.source[0].validator_value == {"required": ["mlm:artifact_type"]} + assert "mlm:artifact_type" in str(exc.value.source[0].validator_value) + assert exc.value.source[0].schema["description"] == "Fields that are disallowed under the Item properties." @pytest.mark.parametrize( From 7afb18809105ac530c8a003cf2ef8f6a3d329648 Mon Sep 17 00:00:00 2001 From: Francis Charette Migneault Date: Wed, 6 Nov 2024 19:48:11 -0500 Subject: [PATCH 56/83] ignore test typing issues related to JSON manipulation --- tests/test_schema.py | 44 +++++++++++++++++++++++++------------------- 1 file changed, 25 insertions(+), 19 deletions(-) diff --git a/tests/test_schema.py b/tests/test_schema.py index f3bb313..7e025f0 100644 --- a/tests/test_schema.py +++ b/tests/test_schema.py @@ -4,6 +4,7 @@ import pystac import pytest +from jsonschema.exceptions import ValidationError from pystac.validation.stac_validator import STACValidator from stac_model.base import JSON @@ -11,6 +12,9 @@ from conftest import get_all_stac_item_examples +# ignore typing errors introduced by generic JSON manipulation errors +# mypy: disable_error_code="arg-type,call-overload,index,union-attr" + @pytest.mark.parametrize( "mlm_example", # value passed to 'mlm_example' fixture @@ -34,7 +38,7 @@ def test_mlm_schema( ) def test_mlm_no_undefined_prefixed_field_item_properties( mlm_validator: STACValidator, - mlm_example: Dict[str, JSON], + mlm_example: dict[str, JSON], ) -> None: mlm_data = copy.deepcopy(mlm_example) mlm_item = pystac.Item.from_dict(mlm_data) @@ -42,7 +46,7 @@ def test_mlm_no_undefined_prefixed_field_item_properties( # undefined property anywhere in the schema mlm_data = copy.deepcopy(mlm_example) - mlm_data["properties"]["mlm:unknown"] = "random" # type: ignore + mlm_data["properties"]["mlm:unknown"] = "random" with pytest.raises(pystac.errors.STACValidationError) as exc: mlm_item = pystac.Item.from_dict(mlm_data) pystac.validation.validate(mlm_item, validator=mlm_validator) @@ -53,12 +57,13 @@ def test_mlm_no_undefined_prefixed_field_item_properties( # defined property only allowed at the Asset level mlm_data = copy.deepcopy(mlm_example) - mlm_data["properties"]["mlm:artifact_type"] = "torch.save" # type: ignore + mlm_data["properties"]["mlm:artifact_type"] = "torch.save" with pytest.raises(pystac.errors.STACValidationError) as exc: mlm_item = pystac.Item.from_dict(mlm_data) pystac.validation.validate(mlm_item, validator=mlm_validator) - assert "mlm:artifact_type" in str(exc.value.source[0].validator_value) - assert exc.value.source[0].schema["description"] == "Fields that are disallowed under the Item properties." + errors = cast(list[ValidationError], exc.value.source) + assert "mlm:artifact_type" in str(errors[0].validator_value) + assert errors[0].schema["description"] == "Fields that are disallowed under the Item properties." @pytest.mark.parametrize( @@ -85,17 +90,17 @@ def test_mlm_no_undefined_prefixed_field_asset_properties( mlm_data = copy.deepcopy(mlm_example) mlm_item = pystac.Item.from_dict(mlm_data) pystac.validation.validate(mlm_item, validator=mlm_validator) # ensure original is valid - assert mlm_data["assets"]["weights"] # type: ignore + assert mlm_data["assets"]["weights"] mlm_data = copy.deepcopy(mlm_example) - mlm_data["assets"]["weights"][test_field] = test_value # type: ignore + mlm_data["assets"]["weights"][test_field] = test_value with pytest.raises(pystac.errors.STACValidationError) as exc: mlm_item = pystac.Item.from_dict(mlm_data) pystac.validation.validate(mlm_item, validator=mlm_validator) - assert len(exc.value.source) == 1 # type: ignore - schema_error = exc.value.source[0] # type: ignore - assert test_field in schema_error.instance - assert schema_error.schema["description"] in [ + assert len(exc.value.source) == 1 + errors = cast(list[ValidationError], exc.value.source) + assert test_field in errors[0].instance + assert errors[0].schema["description"] in [ "All possible MLM fields regardless of the level they apply (Collection, Item, Asset, Link).", "Fields that are disallowed under the Asset properties." ] @@ -112,7 +117,7 @@ def test_mlm_allowed_field_asset_properties_override( ) -> None: # defined property allowed both at the Item at the Asset level mlm_data = copy.deepcopy(mlm_example) - mlm_data["assets"]["weights"]["mlm:accelerator"] = "cuda" # type: ignore + mlm_data["assets"]["weights"]["mlm:accelerator"] = "cuda" mlm_item = pystac.Item.from_dict(mlm_data) pystac.validation.validate(mlm_item, validator=mlm_validator) @@ -130,7 +135,7 @@ def test_mlm_missing_bands_invalid_if_mlm_input_lists_bands( pystac.validation.validate(mlm_item, validator=mlm_validator) # ensure original is valid mlm_bands_bad_data = copy.deepcopy(mlm_example) - mlm_bands_bad_data["assets"]["weights"].pop("raster:bands") # type: ignore # no 'None' to raise in case modified + mlm_bands_bad_data["assets"]["weights"].pop("raster:bands") # no 'None' to raise in case missing with pytest.raises(pystac.errors.STACValidationError): mlm_bands_bad_item = pystac.Item.from_dict(mlm_bands_bad_data) pystac.validation.validate(mlm_bands_bad_item, validator=mlm_validator) @@ -149,7 +154,7 @@ def test_mlm_eo_bands_invalid_only_in_item_properties( pystac.validation.validate(mlm_item, validator=mlm_validator) # ensure original is valid mlm_eo_bands_bad_data = copy.deepcopy(mlm_example) - mlm_eo_bands_bad_data["assets"]["weights"].pop("eo:bands") # type: ignore # no 'None' to raise in case modified + mlm_eo_bands_bad_data["assets"]["weights"].pop("eo:bands") # no 'None' to raise in case missing with pytest.raises(pystac.errors.STACValidationError): mlm_eo_bands_bad_item = pystac.Item.from_dict(mlm_eo_bands_bad_data) pystac.validation.validate(mlm_eo_bands_bad_item, validator=mlm_validator) @@ -165,12 +170,12 @@ def test_mlm_no_input_allowed_but_explicit_empty_array_required( mlm_example: Dict[str, JSON], ) -> None: mlm_data = copy.deepcopy(mlm_example) - mlm_data["properties"]["mlm:input"] = [] # type: ignore + mlm_data["properties"]["mlm:input"] = [] mlm_item = pystac.Item.from_dict(mlm_data) pystac.validation.validate(mlm_item, validator=mlm_validator) with pytest.raises(pystac.errors.STACValidationError): - mlm_data["properties"].pop("mlm:input") # type: ignore # no 'None' to raise in case modified + mlm_data["properties"].pop("mlm:input") # no 'None' to raise in case missing mlm_item = pystac.Item.from_dict(mlm_data) pystac.validation.validate(mlm_item, validator=mlm_validator) @@ -230,13 +235,13 @@ def test_mlm_other_non_mlm_assets_allowed( mlm_item = pystac.Item.from_dict(mlm_data) pystac.validation.validate(mlm_item, validator=mlm_validator) # self-check valid beforehand - mlm_data["assets"]["sample"] = { # type: ignore + mlm_data["assets"]["sample"] = { "type": "image/jpeg", "href": "https://example.com/sample/output.jpg", "roles": ["preview"], "title": "Model Output Predictions Sample", } - mlm_data["assets"]["model-cart"] = { # type: ignore + mlm_data["assets"]["model-cart"] = { "type": "text/markdown", "href": "https://example.com/sample/model.md", "roles": ["metadata"], @@ -285,7 +290,8 @@ def test_mlm_at_least_one_asset_model( else: with pytest.raises(pystac.errors.STACValidationError) as exc: pystac.validation.validate(mlm_item, validator=mlm_validator) - assert exc.value.source[0].schema["$comment"] in [ # type: ignore + errors = cast(list[ValidationError], exc.value.source) + assert errors[0].schema["$comment"] in [ "At least one Asset must provide the model definition indicated by the 'mlm:model' role.", "Used to check the artifact type property that is required by a Model Asset annotated by 'mlm:model' role." ] From 3bcdb87be6f2ab0f03024ad05742cfd43c1e657c Mon Sep 17 00:00:00 2001 From: Francis Charette Migneault Date: Wed, 6 Nov 2024 23:35:26 -0500 Subject: [PATCH 57/83] adjust pydantic fields attributes --- stac_model/output.py | 4 ++-- stac_model/runtime.py | 2 +- 2 files changed, 3 insertions(+), 3 deletions(-) diff --git a/stac_model/output.py b/stac_model/output.py index 0e25ec7..79bc17f 100644 --- a/stac_model/output.py +++ b/stac_model/output.py @@ -7,8 +7,8 @@ class ModelResult(MLMBaseModel): - shape: List[Union[int, float]] = Field(..., min_items=1) - dim_order: List[str] = Field(..., min_items=1) + shape: List[Union[int, float]] = Field(..., min_length=1) + dim_order: List[str] = Field(..., min_length=1) data_type: DataType diff --git a/stac_model/runtime.py b/stac_model/runtime.py index 9104fa6..1fd60d7 100644 --- a/stac_model/runtime.py +++ b/stac_model/runtime.py @@ -46,4 +46,4 @@ class Runtime(MLMBaseModel): accelerator: Optional[AcceleratorType] = Field(default=None) accelerator_constrained: bool = Field(default=False) accelerator_summary: Annotated[Optional[str], OmitIfNone] = Field(default=None) - accelerator_count: Annotated[Optional[int], OmitIfNone] = Field(default=None, minimum=1) + accelerator_count: Annotated[Optional[int], OmitIfNone] = Field(default=None, ge=1) From 1dcbac8069c3640bb49d6fca18d2fcd6a351bd13 Mon Sep 17 00:00:00 2001 From: Francis Charette Migneault Date: Wed, 6 Nov 2024 23:35:57 -0500 Subject: [PATCH 58/83] update packages --- poetry.lock | 1330 ++++++++++++++++++++++++++------------------------- 1 file changed, 690 insertions(+), 640 deletions(-) diff --git a/poetry.lock b/poetry.lock index 5f502a0..c750465 100644 --- a/poetry.lock +++ b/poetry.lock @@ -1,4 +1,4 @@ -# This file is automatically @generated by Poetry 1.8.3 and should not be changed by hand. +# This file is automatically @generated by Poetry 1.8.4 and should not be changed by hand. [[package]] name = "annotated-types" @@ -13,32 +13,32 @@ files = [ [[package]] name = "attrs" -version = "23.2.0" +version = "24.2.0" description = "Classes Without Boilerplate" optional = false python-versions = ">=3.7" files = [ - {file = "attrs-23.2.0-py3-none-any.whl", hash = "sha256:99b87a485a5820b23b879f04c2305b44b951b502fd64be915879d77a7e8fc6f1"}, - {file = "attrs-23.2.0.tar.gz", hash = "sha256:935dc3b529c262f6cf76e50877d35a4bd3c1de194fd41f47a2b7ae8f19971f30"}, + {file = "attrs-24.2.0-py3-none-any.whl", hash = "sha256:81921eb96de3191c8258c199618104dd27ac608d9366f5e35d011eae1867ede2"}, + {file = "attrs-24.2.0.tar.gz", hash = "sha256:5cfb1b9148b5b086569baec03f20d7b6bf3bcacc9a42bebf87ffaaca362f6346"}, ] [package.extras] -cov = ["attrs[tests]", "coverage[toml] (>=5.3)"] -dev = ["attrs[tests]", "pre-commit"] -docs = ["furo", "myst-parser", "sphinx", "sphinx-notfound-page", "sphinxcontrib-towncrier", "towncrier", "zope-interface"] -tests = ["attrs[tests-no-zope]", "zope-interface"] -tests-mypy = ["mypy (>=1.6)", "pytest-mypy-plugins"] -tests-no-zope = ["attrs[tests-mypy]", "cloudpickle", "hypothesis", "pympler", "pytest (>=4.3.0)", "pytest-xdist[psutil]"] +benchmark = ["cloudpickle", "hypothesis", "mypy (>=1.11.1)", "pympler", "pytest (>=4.3.0)", "pytest-codspeed", "pytest-mypy-plugins", "pytest-xdist[psutil]"] +cov = ["cloudpickle", "coverage[toml] (>=5.3)", "hypothesis", "mypy (>=1.11.1)", "pympler", "pytest (>=4.3.0)", "pytest-mypy-plugins", "pytest-xdist[psutil]"] +dev = ["cloudpickle", "hypothesis", "mypy (>=1.11.1)", "pre-commit", "pympler", "pytest (>=4.3.0)", "pytest-mypy-plugins", "pytest-xdist[psutil]"] +docs = ["cogapp", "furo", "myst-parser", "sphinx", "sphinx-notfound-page", "sphinxcontrib-towncrier", "towncrier (<24.7)"] +tests = ["cloudpickle", "hypothesis", "mypy (>=1.11.1)", "pympler", "pytest (>=4.3.0)", "pytest-mypy-plugins", "pytest-xdist[psutil]"] +tests-mypy = ["mypy (>=1.11.1)", "pytest-mypy-plugins"] [[package]] name = "bandit" -version = "1.7.8" +version = "1.7.10" description = "Security oriented static analyser for python code." optional = false python-versions = ">=3.8" files = [ - {file = "bandit-1.7.8-py3-none-any.whl", hash = "sha256:509f7af645bc0cd8fd4587abc1a038fc795636671ee8204d502b933aee44f381"}, - {file = "bandit-1.7.8.tar.gz", hash = "sha256:36de50f720856ab24a24dbaa5fee2c66050ed97c1477e0a1159deab1775eab6b"}, + {file = "bandit-1.7.10-py3-none-any.whl", hash = "sha256:665721d7bebbb4485a339c55161ac0eedde27d51e638000d91c8c2d68343ad02"}, + {file = "bandit-1.7.10.tar.gz", hash = "sha256:59ed5caf5d92b6ada4bf65bc6437feea4a9da1093384445fed4d472acc6cff7b"}, ] [package.dependencies] @@ -56,13 +56,13 @@ yaml = ["PyYAML"] [[package]] name = "bracex" -version = "2.4" +version = "2.5.post1" description = "Bash style brace expander." optional = false python-versions = ">=3.8" files = [ - {file = "bracex-2.4-py3-none-any.whl", hash = "sha256:efdc71eff95eaff5e0f8cfebe7d01adf2c8637c8c92edaf63ef348c241a82418"}, - {file = "bracex-2.4.tar.gz", hash = "sha256:a27eaf1df42cf561fed58b7a8f3fdf129d1ea16a81e1fadd1d17989bc6384beb"}, + {file = "bracex-2.5.post1-py3-none-any.whl", hash = "sha256:13e5732fec27828d6af308628285ad358047cec36801598368cb28bc631dbaf6"}, + {file = "bracex-2.5.post1.tar.gz", hash = "sha256:12c50952415bfa773d2d9ccb8e79651b8cdb1f31a42f6091b804f6ba2b4a66b6"}, ] [[package]] @@ -93,13 +93,13 @@ test = ["coverage", "freezegun", "pre-commit", "pytest", "pytest-cov", "pytest-m [[package]] name = "certifi" -version = "2024.2.2" +version = "2024.8.30" description = "Python package for providing Mozilla's CA Bundle." optional = false python-versions = ">=3.6" files = [ - {file = "certifi-2024.2.2-py3-none-any.whl", hash = "sha256:dc383c07b76109f368f6106eee2b593b04a011ea4d55f652c6ca24a754d1cdd1"}, - {file = "certifi-2024.2.2.tar.gz", hash = "sha256:0569859f95fc761b18b45ef421b1290a0f65f147e92a1e5eb3e635f9a5e4e66f"}, + {file = "certifi-2024.8.30-py3-none-any.whl", hash = "sha256:922820b53db7a7257ffbda3f597266d435245903d80737e34f8a45ff3e3230d8"}, + {file = "certifi-2024.8.30.tar.gz", hash = "sha256:bec941d2aa8195e248a60b31ff9f0558284cf01a52591ceda73ea9afffd69fd9"}, ] [[package]] @@ -115,101 +115,116 @@ files = [ [[package]] name = "charset-normalizer" -version = "3.3.2" +version = "3.4.0" description = "The Real First Universal Charset Detector. 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[[package]] name = "typing-extensions" -version = "4.11.0" +version = "4.12.2" description = "Backported and Experimental Type Hints for Python 3.8+" optional = false python-versions = ">=3.8" files = [ - {file = "typing_extensions-4.11.0-py3-none-any.whl", hash = "sha256:c1f94d72897edaf4ce775bb7558d5b79d8126906a14ea5ed1635921406c0387a"}, - {file = "typing_extensions-4.11.0.tar.gz", hash = "sha256:83f085bd5ca59c80295fc2a82ab5dac679cbe02b9f33f7d83af68e241bea51b0"}, + {file = "typing_extensions-4.12.2-py3-none-any.whl", hash = "sha256:04e5ca0351e0f3f85c6853954072df659d0d13fac324d0072316b67d7794700d"}, + {file = "typing_extensions-4.12.2.tar.gz", hash = "sha256:1a7ead55c7e559dd4dee8856e3a88b41225abfe1ce8df57b7c13915fe121ffb8"}, ] [[package]] name = "urllib3" -version = "2.2.1" +version = "2.2.3" description = "HTTP library with thread-safe connection pooling, file post, and more." optional = false python-versions = ">=3.8" files = [ - {file = "urllib3-2.2.1-py3-none-any.whl", hash = "sha256:450b20ec296a467077128bff42b73080516e71b56ff59a60a02bef2232c4fa9d"}, - {file = "urllib3-2.2.1.tar.gz", hash = "sha256:d0570876c61ab9e520d776c38acbbb5b05a776d3f9ff98a5c8fd5162a444cf19"}, + {file = "urllib3-2.2.3-py3-none-any.whl", hash = "sha256:ca899ca043dcb1bafa3e262d73aa25c465bfb49e0bd9dd5d59f1d0acba2f8fac"}, + {file = "urllib3-2.2.3.tar.gz", hash = "sha256:e7d814a81dad81e6caf2ec9fdedb284ecc9c73076b62654547cc64ccdcae26e9"}, ] [package.extras] @@ -1658,13 +1708,13 @@ zstd = ["zstandard (>=0.18.0)"] [[package]] name = "virtualenv" -version = "20.26.1" +version = "20.27.1" description = "Virtual Python Environment builder" optional = false -python-versions = ">=3.7" +python-versions = ">=3.8" files = [ - {file = "virtualenv-20.26.1-py3-none-any.whl", hash = "sha256:7aa9982a728ae5892558bff6a2839c00b9ed145523ece2274fad6f414690ae75"}, - {file = "virtualenv-20.26.1.tar.gz", hash = "sha256:604bfdceaeece392802e6ae48e69cec49168b9c5f4a44e483963f9242eb0e78b"}, + {file = "virtualenv-20.27.1-py3-none-any.whl", hash = "sha256:f11f1b8a29525562925f745563bfd48b189450f61fb34c4f9cc79dd5aa32a1f4"}, + {file = "virtualenv-20.27.1.tar.gz", hash = "sha256:142c6be10212543b32c6c45d3d3893dff89112cc588b7d0879ae5a1ec03a47ba"}, ] [package.dependencies] From d4f8c48d1081bfdab234ef2605c8fa7116f08761 Mon Sep 17 00:00:00 2001 From: Francis Charette Migneault Date: Wed, 6 Nov 2024 23:49:14 -0500 Subject: [PATCH 59/83] ignore inconsistent pydantic/mypy typing between versions --- stac_model/schema.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/stac_model/schema.py b/stac_model/schema.py index 63310ba..008d7ec 100644 --- a/stac_model/schema.py +++ b/stac_model/schema.py @@ -173,7 +173,7 @@ class SummariesMLModelExtension(SummariesExtension): def _check_mlm_property(self, prop: str) -> FieldInfo: try: - return MLModelProperties.model_fields[prop] + return MLModelProperties.model_fields[prop] # type: ignore except KeyError as err: raise AttributeError(f"Name '{prop}' is not a valid MLM property.") from err From 9c18aba6f891a386a4e887c52e187ab237dc3db3 Mon Sep 17 00:00:00 2001 From: Ryan Avery Date: Mon, 11 Nov 2024 11:27:21 -0800 Subject: [PATCH 60/83] update setup command, add back dependabot --- .github/dependabot.yml | 35 +++++++++++++++++++++++++++++++++++ Makefile | 20 ++++++++++---------- 2 files changed, 45 insertions(+), 10 deletions(-) create mode 100644 .github/dependabot.yml diff --git a/.github/dependabot.yml b/.github/dependabot.yml new file mode 100644 index 0000000..8f872f1 --- /dev/null +++ b/.github/dependabot.yml @@ -0,0 +1,35 @@ +# Configuration: https://dependabot.com/docs/config-file/ +# Docs: https://docs.github.com/en/github/administering-a-repository/keeping-your-dependencies-updated-automatically + +version: 2 + +updates: + - package-ecosystem: "pip" + directory: "/" + schedule: + interval: "monthly" + allow: + - dependency-type: "all" + commit-message: + prefix: ":arrow_up:" + open-pull-requests-limit: 5 + + - package-ecosystem: "github-actions" + directory: "/" + schedule: + interval: "monthly" + allow: + - dependency-type: "all" + commit-message: + prefix: ":arrow_up:" + open-pull-requests-limit: 5 + + - package-ecosystem: "docker" + directory: "/docker" + schedule: + interval: "monthly" + allow: + - dependency-type: "all" + commit-message: + prefix: ":arrow_up:" + open-pull-requests-limit: 5 diff --git a/Makefile b/Makefile index f7733f9..301aea7 100644 --- a/Makefile +++ b/Makefile @@ -5,7 +5,7 @@ ACTIVEPYTHON = $(shell which python) #* UV .PHONY: setup setup: - curl -LsSf https://astral.sh/uv/install.sh | sh + which uv >/dev/null || (curl -LsSf https://astral.sh/uv/install.sh | sh) .PHONY: publish publish: @@ -13,22 +13,22 @@ publish: #* Installation .PHONY: install -install: +install: setup uv export --format requirements-txt -o requirements.txt --no-dev uv pip install --python $(ACTIVEPYTHON) -r requirements.txt .PHONY: install-dev -install-dev: +install-dev: setup uv export --format requirements-txt -o requirements-dev.txt uv pip install --python $(ACTIVEPYTHON) -r requirements-dev.txt .PHONY: pre-commit-install -pre-commit-install: +pre-commit-install: setup uv run --python $(ACTIVEPYTHON) pre-commit install #* Formatters .PHONY: codestyle -codestyle: +codestyle: setup uv run --python $(ACTIVEPYTHON) ruff format --config=pyproject.toml stac_model tests .PHONY: format @@ -36,7 +36,7 @@ format: codestyle #* Linting .PHONY: test -test: +test: setup uv run --python $(ACTIVEPYTHON) pytest -c pyproject.toml --cov-report=html --cov=stac_model tests/ .PHONY: check @@ -46,19 +46,19 @@ check: check-examples check-markdown check-lint check-mypy check-safety check-ci check-all: check .PHONY: mypy -mypy: +mypy: setup uv run --python $(ACTIVEPYTHON) mypy --config-file pyproject.toml ./ .PHONY: check-mypy check-mypy: mypy .PHONY: check-safety -check-safety: +check-safety: setup uv run --python $(ACTIVEPYTHON) safety check --full-report uv run --python $(ACTIVEPYTHON) bandit -ll --recursive stac_model tests .PHONY: lint -lint: +lint: setup uv run --python $(ACTIVEPYTHON) ruff check --fix --config=pyproject.toml ./ .PHONY: check-lint @@ -96,7 +96,7 @@ $(addprefix fix-, $(FORMATTERS)): fix-%: format-% lint-all: lint mypy check-safety check-markdown .PHONY: update-dev-deps -update-dev-deps: +update-dev-deps: setup uv export --only-dev --format requirements-txt -o requirements-only-dev.txt uv pip install --python $(ACTIVEPYTHON) -r requirements-only-dev.txt From f70b450f1e5664dac781f95244d6fe37ac64cac1 Mon Sep 17 00:00:00 2001 From: Francis Charette Migneault Date: Mon, 11 Nov 2024 16:51:05 -0500 Subject: [PATCH 61/83] better MLM Asset roles descriptions (fixes https://github.com/stac-extensions/mlm/issues/54) --- CHANGELOG.md | 2 ++ README.md | 21 ++++++++++++++++++--- 2 files changed, 20 insertions(+), 3 deletions(-) diff --git a/CHANGELOG.md b/CHANGELOG.md index 2dc9d3f..9a50648 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -8,6 +8,8 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0 ## [Unreleased](https://github.com/stac-extensions/mlm/tree/main) ### Added +- Add better descriptions about required and recommended *MLM Asset Roles* and their implications + (fixes [#54](https://github.com/stac-extensions/mlm/issues/54)). - Add explicit check of `value_scaling` sub-fields `minimum`, `maximum`, `mean`, `stddev`, etc. for corresponding `type` values `min-max` and `z-score` that depend on it. - Allow different `value_scaling` operations per band/channel/dimension as needed by the model. diff --git a/README.md b/README.md index 265ac4d..5a5c6cb 100644 --- a/README.md +++ b/README.md @@ -595,6 +595,21 @@ names for nesting the Assets in order to improve their quick identification, alt left up to user preference. However, the MLM Asset definitions **MUST** include the appropriate [MLM Asset Roles](#mlm-asset-roles) to ensure their discovery. +A valid STAC MLM Item definition requires at least one Asset with the `mlm:model` role, as well as, +an accompanying `mlm:artifact_type` property that describes how to employ it. +An Asset described with this role is considered the "*main*" [Model Asset](#model-asset) being described by +the STAC Item definition. Typically, there will be only one asset containing the `mlm:model` role. +However, multiple `mlm:model` are permitted to provide alternate interfaces of the same model +(e.g.: using different frameworks or compilations), but those assets should have exactly the same model interfaces +(i.e.: identical `mlm:input`, `mlm:output`, etc.). In such case, the `mlm:artifact_type` property should be used to +distinguish them. + +Additional definitions such as the [Source Code Asset](#source-code-asset) and the [Container Asset](#container-asset) +are considered "*side-car*" Assets that help understand how to employ the model, such as through the reference training +script that produced the model or a preconfigured inference runtime environment. These additional Assets are optional, +but it is *STRONGLY RECOMMENDED* to provide them in order to help correct adoption and use of the described model +by users. + [stac-asset]: https://github.com/radiantearth/stac-spec/blob/master/collection-spec/collection-spec.md#asset-object ### MLM Asset Roles @@ -617,8 +632,8 @@ In order to provide more context, the following roles are also recommended were | mlm:training-runtime (*) | `runtime` | Describes an Asset that provides runtime reference to perform model training. | | mlm:checkpoint (*) | `weights`, `checkpoint` | Describes an Asset that provides a model checkpoint with embedded model configurations. | | mlm:weights | `weights`, `checkpoint` | Describes an Asset that provides a model weights (typically some Tensor representation). | -| mlm:model | `model` | Required role for [Model Asset](#model-asset). | -| mlm:source_code | `code` | Required role for [Model Asset](#source-code-asset). | +| mlm:model | `model` | **REQUIRED** Role for [Model Asset](#model-asset). | +| mlm:source_code | `code` | **RECOMMENDED** Role for [Source Code Asset](#source-code-asset). | @@ -642,7 +657,7 @@ In order to provide more context, the following roles are also recommended were Recommended Asset `roles` include `mlm:weights` or `mlm:checkpoint` for model weights that need to be loaded by a model definition and `mlm:compiled` for models that can be loaded directly without an intermediate model definition. -In each case, the `mlm:model` should be applied as well to indicate that this asset represents the model. +In each case, the `mlm:model` **MUST** be applied as well to indicate that this asset represents the model. It is also recommended to make use of the [file](https://github.com/stac-extensions/file?tab=readme-ov-file#asset--link-object-fields) From 021d0a2a126ecc4d7633da46b241af28198ffa8b Mon Sep 17 00:00:00 2001 From: Ryan Avery Date: Mon, 11 Nov 2024 14:52:22 -0800 Subject: [PATCH 62/83] add backticks, remove extra | None --- CONTRIBUTING.md | 4 ++-- stac_model/input.py | 1 - 2 files changed, 2 insertions(+), 3 deletions(-) diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md index 47b5169..07a47c9 100644 --- a/CONTRIBUTING.md +++ b/CONTRIBUTING.md @@ -11,7 +11,7 @@ make setup > This installs uv as a [standalone application][uv-install].
> For more details, see also the [uv documentation][uv-docs].
-2. Initialize project dependencies with uv and install `pre-commit` hooks: +2. Initialize project dependencies with `uv` and install `pre-commit` hooks: ```bash make install-dev @@ -19,7 +19,7 @@ make pre-commit-install ``` This will install project dependencies into the currently active environment. If you would like to -use uv's default behavior of managing a project-scoped environment, use uv commands directly to +use uv's default behavior of managing a project-scoped environment, use `uv` commands directly to install dependencies. `uv sync` will install dependencies and dev dependencies in `.venv` and update the `uv.lock`. ## PR submission diff --git a/stac_model/input.py b/stac_model/input.py index 2989f2f..c0a2972 100644 --- a/stac_model/input.py +++ b/stac_model/input.py @@ -71,7 +71,6 @@ class ValueScalingProcessingExpression(ProcessingExpression): ValueScalingScale, ValueScalingProcessingExpression, ] - | None ] # noqa: E501 ResizeType: TypeAlias = ( From eb2e5c62ba7d33bbbf0f39fab737d56f453c6ab8 Mon Sep 17 00:00:00 2001 From: Ryan Avery Date: Tue, 12 Nov 2024 10:20:35 -0800 Subject: [PATCH 63/83] remove noqa, make format --- stac_model/examples.py | 2 +- stac_model/input.py | 5 +++-- tests/test_schema.py | 11 +++++++---- 3 files changed, 11 insertions(+), 7 deletions(-) diff --git a/stac_model/examples.py b/stac_model/examples.py index 731830b..e5047cc 100644 --- a/stac_model/examples.py +++ b/stac_model/examples.py @@ -70,7 +70,7 @@ def eurosat_resnet() -> ItemMLModelExtension: type="z-score", mean=mean, stddev=stddev, - ) + ), ) for mean, stddev in zip(stats_mean, stats_stddev, strict=False) ] diff --git a/stac_model/input.py b/stac_model/input.py index c0a2972..e69d8c0 100644 --- a/stac_model/input.py +++ b/stac_model/input.py @@ -18,6 +18,7 @@ def validate_dimensions(self) -> Self: raise ValueError("Dimension order and shape must be of equal length for corresponding indices.") return self + class ValueScalingClipMin(MLMBaseModel): type: Literal["clip-min"] = "clip-min" minimum: Number @@ -71,7 +72,7 @@ class ValueScalingProcessingExpression(ProcessingExpression): ValueScalingScale, ValueScalingProcessingExpression, ] - ] # noqa: E501 +] ResizeType: TypeAlias = ( Literal[ @@ -87,7 +88,7 @@ class ValueScalingProcessingExpression(ProcessingExpression): "wrap-inverse-map", ] | None -) # noqa: E501 +) class ModelBand(MLMBaseModel): diff --git a/tests/test_schema.py b/tests/test_schema.py index 5e41393..53addef 100644 --- a/tests/test_schema.py +++ b/tests/test_schema.py @@ -122,10 +122,13 @@ def test_mlm_no_input_allowed_but_explicit_empty_array_required( ([{"type": "z-score", "mean": 1, "stddev": 2, "minimum": 1, "maximum": 2}], True), # extra must be ignored ([{"type": "processing"}], False), ([{"type": "processing", "format": "test", "expression": "test"}], True), - ([ - {"type": "processing", "format": "test", "expression": "test"}, - {"type": "min-max", "minimum": 1, "maximum": 2} - ], True), + ( + [ + {"type": "processing", "format": "test", "expression": "test"}, + {"type": "min-max", "minimum": 1, "maximum": 2}, + ], + True, + ), ], ) def test_mlm_input_scaling_combination( From 9752f5ef02fc9a86279b0cee4ded409098e0a5f4 Mon Sep 17 00:00:00 2001 From: Ryan Avery Date: Tue, 12 Nov 2024 13:46:02 -0800 Subject: [PATCH 64/83] backticks, use Optional instead of | None --- CONTRIBUTING.md | 6 +++--- stac_model/input.py | 9 ++++----- 2 files changed, 7 insertions(+), 8 deletions(-) diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md index 07a47c9..ddb3127 100644 --- a/CONTRIBUTING.md +++ b/CONTRIBUTING.md @@ -2,14 +2,14 @@ ## Project setup -1. If you don't have uv installed run: +1. If you don't have `uv` installed run: ```bash make setup ``` -> This installs uv as a [standalone application][uv-install].
-> For more details, see also the [uv documentation][uv-docs].
+> This installs `uv` as a [standalone application][uv-install].
+> For more details, see also the [`uv` documentation][uv-docs].
2. Initialize project dependencies with `uv` and install `pre-commit` hooks: diff --git a/stac_model/input.py b/stac_model/input.py index e69d8c0..7a189d2 100644 --- a/stac_model/input.py +++ b/stac_model/input.py @@ -74,7 +74,7 @@ class ValueScalingProcessingExpression(ProcessingExpression): ] ] -ResizeType: TypeAlias = ( +ResizeType: TypeAlias = Optional[ Literal[ "crop", "pad", @@ -87,8 +87,7 @@ class ValueScalingProcessingExpression(ProcessingExpression): "wrap-fill-outliers", "wrap-inverse-map", ] - | None -) +] class ModelBand(MLMBaseModel): @@ -99,11 +98,11 @@ class ModelBand(MLMBaseModel): ) ) # similar to 'ProcessingExpression', but they can be omitted here - format: Annotated[str | None, OmitIfNone] = Field( + format: Annotated[Optional[str], OmitIfNone] = Field( default=None, description="", ) - expression: Annotated[Any | None, OmitIfNone] = Field( + expression: Annotated[Optional[Any], OmitIfNone] = Field( default=None, description="", ) From d564b2d9c3e257cf9880e7f75fcc178519ace7da Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Wed, 13 Nov 2024 15:14:12 +0000 Subject: [PATCH 65/83] :arrow_up: Bump actions/cache from 4.0.2 to 4.1.2 Bumps [actions/cache](https://github.com/actions/cache) from 4.0.2 to 4.1.2. - [Release notes](https://github.com/actions/cache/releases) - [Changelog](https://github.com/actions/cache/blob/main/RELEASES.md) - [Commits](https://github.com/actions/cache/compare/v4.0.2...v4.1.2) --- updated-dependencies: - dependency-name: actions/cache dependency-type: direct:production update-type: version-update:semver-minor ... Signed-off-by: dependabot[bot] --- .github/workflows/stac-model.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/workflows/stac-model.yml b/.github/workflows/stac-model.yml index 5d42720..42b52a6 100644 --- a/.github/workflows/stac-model.yml +++ b/.github/workflows/stac-model.yml @@ -20,7 +20,7 @@ jobs: run: make setup - name: Set up cache - uses: actions/cache@v4.0.2 + uses: actions/cache@v4.1.2 with: path: .venv key: venv-${{ matrix.python-version }}-${{ hashFiles('pyproject.toml') }}-${{ hashFiles('uv.lock') }} From fb1ad158cb93424d60109c7be13a5c98625c5591 Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Wed, 13 Nov 2024 15:14:14 +0000 Subject: [PATCH 66/83] :arrow_up: Bump actions/setup-python from 5.2.0 to 5.3.0 Bumps [actions/setup-python](https://github.com/actions/setup-python) from 5.2.0 to 5.3.0. - [Release notes](https://github.com/actions/setup-python/releases) - [Commits](https://github.com/actions/setup-python/compare/v5.2.0...v5.3.0) --- updated-dependencies: - dependency-name: actions/setup-python dependency-type: direct:production update-type: version-update:semver-minor ... Signed-off-by: dependabot[bot] --- .github/workflows/publish.yaml | 2 +- .github/workflows/stac-model.yml | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/.github/workflows/publish.yaml b/.github/workflows/publish.yaml index ff89a22..65b78fe 100644 --- a/.github/workflows/publish.yaml +++ b/.github/workflows/publish.yaml @@ -22,7 +22,7 @@ jobs: steps: - uses: actions/checkout@v4 - name: Set up Python - uses: actions/setup-python@v5.2.0 + uses: actions/setup-python@v5.3.0 with: python-version: "3.10" - name: Install uv diff --git a/.github/workflows/stac-model.yml b/.github/workflows/stac-model.yml index 5d42720..bf4198a 100644 --- a/.github/workflows/stac-model.yml +++ b/.github/workflows/stac-model.yml @@ -12,7 +12,7 @@ jobs: steps: - uses: actions/checkout@v4 - name: Set up Python ${{ matrix.python-version }} - uses: actions/setup-python@v5.2.0 + uses: actions/setup-python@v5.3.0 with: python-version: ${{ matrix.python-version }} From c6403f4fc422fdd5d77677a0c349aa5dc15e5bbe Mon Sep 17 00:00:00 2001 From: Francis Charette Migneault Date: Wed, 13 Nov 2024 11:07:15 -0500 Subject: [PATCH 67/83] fix unused typing ignore error from mypy --- stac_model/schema.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/stac_model/schema.py b/stac_model/schema.py index 3b0ee0d..e2d2b5a 100644 --- a/stac_model/schema.py +++ b/stac_model/schema.py @@ -167,7 +167,7 @@ class SummariesMLModelExtension(SummariesExtension): def _check_mlm_property(self, prop: str) -> FieldInfo: try: - return MLModelProperties.model_fields[prop] # type: ignore + return MLModelProperties.model_fields[prop] except KeyError as err: raise AttributeError(f"Name '{prop}' is not a valid MLM property.") from err From be0a95313134cb5a344b02208dc8ab0bf75df99b Mon Sep 17 00:00:00 2001 From: Francis Charette Migneault Date: Wed, 13 Nov 2024 17:06:10 -0500 Subject: [PATCH 68/83] fix makefile to allow reuse of conda/virtual env --- Makefile | 43 +++++++++++++++++++++++++++---------------- 1 file changed, 27 insertions(+), 16 deletions(-) diff --git a/Makefile b/Makefile index 301aea7..72c9377 100644 --- a/Makefile +++ b/Makefile @@ -1,6 +1,17 @@ #* Variables SHELL ?= /usr/bin/env bash -ACTIVEPYTHON = $(shell which python) +PYTHON_PATH = $(shell which python) +PYTHON_ROOT := $(dir $(PYTHON_PATH)) +# use the directory rather than the python binary to allow auto-discovery, which is more cross-platform compatible +UV_PYTHON_PATH ?= $(UV_PYTHON_ROOT) +# to actually reuse an existing virtual/conda environment, the 'UV_PROJECT_ENVIRONMENT' variable must be set to it +# use this command: +# UV_PROJECT_ENVIRONMENT=/path/to/env make [target] +# consider exporting this variable in '/path/to/env/etc/conda/activate.d/env.sh' to enable it by default when +# activating a conda environment, and reset it in '/path/to/env/etc/conda/deactivate.d/env.sh' +UV_PROJECT_ENVIRONMENT ?= .venv +# make sure every uv command employs the specified environment path +UV_COMMAND ?= UV_PROJECT_ENVIRONMENT="$(UV_PROJECT_ENVIRONMENT)" uv #* UV .PHONY: setup @@ -9,27 +20,27 @@ setup: .PHONY: publish publish: - uv publish --build + $(UV_COMMAND) publish --build #* Installation .PHONY: install install: setup - uv export --format requirements-txt -o requirements.txt --no-dev - uv pip install --python $(ACTIVEPYTHON) -r requirements.txt + $(UV_COMMAND) export --format requirements-txt -o requirements.txt --no-dev + $(UV_COMMAND) pip install --python "$(UV_PYTHON_ROOT)" -r requirements.txt .PHONY: install-dev install-dev: setup - uv export --format requirements-txt -o requirements-dev.txt - uv pip install --python $(ACTIVEPYTHON) -r requirements-dev.txt + $(UV_COMMAND) export --format requirements-txt -o requirements-dev.txt + $(UV_COMMAND) pip install --python "$(UV_PYTHON_ROOT)" -r requirements-dev.txt .PHONY: pre-commit-install pre-commit-install: setup - uv run --python $(ACTIVEPYTHON) pre-commit install + $(UV_COMMAND) run --python "$(UV_PYTHON_ROOT)" pre-commit install #* Formatters .PHONY: codestyle codestyle: setup - uv run --python $(ACTIVEPYTHON) ruff format --config=pyproject.toml stac_model tests + $(UV_COMMAND) run --python "$(UV_PYTHON_ROOT)" ruff format --config=pyproject.toml stac_model tests .PHONY: format format: codestyle @@ -37,7 +48,7 @@ format: codestyle #* Linting .PHONY: test test: setup - uv run --python $(ACTIVEPYTHON) pytest -c pyproject.toml --cov-report=html --cov=stac_model tests/ + $(UV_COMMAND) run --python "$(UV_PYTHON_ROOT)" pytest -c pyproject.toml --cov-report=html --cov=stac_model tests/ .PHONY: check check: check-examples check-markdown check-lint check-mypy check-safety check-citation @@ -47,23 +58,23 @@ check-all: check .PHONY: mypy mypy: setup - uv run --python $(ACTIVEPYTHON) mypy --config-file pyproject.toml ./ + $(UV_COMMAND) run --python "$(UV_PYTHON_ROOT)" mypy --config-file pyproject.toml ./ .PHONY: check-mypy check-mypy: mypy .PHONY: check-safety check-safety: setup - uv run --python $(ACTIVEPYTHON) safety check --full-report - uv run --python $(ACTIVEPYTHON) bandit -ll --recursive stac_model tests + $(UV_COMMAND) run --python "$(UV_PYTHON_ROOT)" safety check --full-report + $(UV_COMMAND) run --python "$(UV_PYTHON_ROOT)" bandit -ll --recursive stac_model tests .PHONY: lint lint: setup - uv run --python $(ACTIVEPYTHON) ruff check --fix --config=pyproject.toml ./ + $(UV_COMMAND) run --python "$(UV_PYTHON_ROOT)" ruff check --fix --config=pyproject.toml ./ .PHONY: check-lint check-lint: lint - uv run --python $(ACTIVEPYTHON) ruff check --config=pyproject.toml ./ + $(UV_COMMAND) run --python "$(UV_PYTHON_ROOT)" ruff check --config=pyproject.toml ./ .PHONY: format-lint format-lint: lint @@ -97,8 +108,8 @@ lint-all: lint mypy check-safety check-markdown .PHONY: update-dev-deps update-dev-deps: setup - uv export --only-dev --format requirements-txt -o requirements-only-dev.txt - uv pip install --python $(ACTIVEPYTHON) -r requirements-only-dev.txt + 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+PYTHON_ROOT := $(shell dirname $(dir $(PYTHON_PATH))) +UV_PYTHON_ROOT ?= $(PYTHON_ROOT) + # to actually reuse an existing virtual/conda environment, the 'UV_PROJECT_ENVIRONMENT' variable must be set to it # use this command: # UV_PROJECT_ENVIRONMENT=/path/to/env make [target] # consider exporting this variable in '/path/to/env/etc/conda/activate.d/env.sh' to enable it by default when # activating a conda environment, and reset it in '/path/to/env/etc/conda/deactivate.d/env.sh' -UV_PROJECT_ENVIRONMENT ?= .venv +UV_PROJECT_ENVIRONMENT ?= # make sure every uv command employs the specified environment path -UV_COMMAND ?= UV_PROJECT_ENVIRONMENT="$(UV_PROJECT_ENVIRONMENT)" uv +ifeq (${UV_PROJECT_ENVIRONMENT},) + UV_COMMAND := uv +else + UV_COMMAND := UV_PROJECT_ENVIRONMENT="${UV_PROJECT_ENVIRONMENT}" uv +endif #* UV .PHONY: setup diff --git a/uv.lock b/uv.lock index 43b668a..09a9ba4 100644 --- a/uv.lock +++ b/uv.lock @@ -258,15 +258,15 @@ wheels = [ [[package]] name = "dparse" -version = 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An Asset described with this role is considered the "*main*" [Model Asset](#model-asset) being described by the STAC Item definition. Typically, there will be only one asset containing the `mlm:model` role. -However, multiple `mlm:model` are permitted to provide alternate interfaces of the same model +However, multiple `mlm:model` roles are permitted to provide alternate interfaces of the same model (e.g.: using different frameworks or compilations), but those assets should have exactly the same model interfaces (i.e.: identical `mlm:input`, `mlm:output`, etc.). In such case, the `mlm:artifact_type` property should be used to distinguish them. From e8db76b51373751c15a04e2f2165e7c3e4c3cb83 Mon Sep 17 00:00:00 2001 From: Francis Charette Migneault Date: Thu, 14 Nov 2024 09:10:31 -0500 Subject: [PATCH 71/83] improve description from PR review comment --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index c45754b..0327f47 100644 --- a/README.md +++ b/README.md @@ -599,7 +599,7 @@ A valid STAC MLM Item definition requires at least one Asset with the `mlm:model an accompanying `mlm:artifact_type` property that describes how to employ it. An Asset described with this role is considered the "*main*" [Model Asset](#model-asset) being described by the STAC Item definition. Typically, there will be only one asset containing the `mlm:model` role. -However, multiple `mlm:model` roles are permitted to provide alternate interfaces of the same model +However, multiple Assets employing the `mlm:model` role are permitted to provide alternate interfaces of the same model (e.g.: using different frameworks or compilations), but those assets should have exactly the same model interfaces (i.e.: identical `mlm:input`, `mlm:output`, etc.). In such case, the `mlm:artifact_type` property should be used to distinguish them. From b4885f6c07c4efd346b5502680865f24161973ec Mon Sep 17 00:00:00 2001 From: Ryan Avery Date: Thu, 12 Dec 2024 15:58:36 -0800 Subject: [PATCH 72/83] update TF/Keras formats using the saving method as the artifact type --- README.md | 27 +++++++++++++++------------ 1 file changed, 15 insertions(+), 12 deletions(-) diff --git a/README.md b/README.md index 423139f..0707203 100644 --- a/README.md +++ b/README.md @@ -655,26 +655,29 @@ Following are some proposed *Artifact Type* values for corresponding approaches, permitted as well. Note that the names are selected using the framework-specific definitions to help the users understand the source explicitly, although this is not strictly required either. -| Artifact Type | Description | -|--------------------|--------------------------------------------------------------------------------------| -| `torch.save` | A [serialized python pickle object][pytorch-save] (i.e.: `.pt`) which can represent a model or state_dict. | -| `torch.jit.save` | A [`TorchScript`][pytorch-jit-script] model artifact obtained with one or more of the graph export options Torchscript Tracing and Torchscript Scripting. | -| `torch.export.save` | A model artifact storing an ExportedProgram obtained by [`torch.export.export`][pytorch-export] (i.e.: `.pt2`). | -| `TFSavedModel` | A [SavedModel][tf-save] from Tensorflow or Keras. | -| `Keras_v3` | Keras v3 is the [recommended format][keras-recommended] by the Tensorflow team. See this example to [save and load models][keras-example] and the update to date docs [disambiguating different save methods][keras-methods] in TF and Keras.. | -| `h5` | [Keras and tf.keras model][h5] weights format, which uses HDF5. | +| Artifact Type | Description | +|--------------------------------------------------|---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| +| `torch.save` | A [serialized python pickle object][pytorch-save] (i.e.: `.pt`) which can represent a model or state_dict. | +| `torch.jit.save` | A [`TorchScript`][pytorch-jit-script] model artifact obtained with one or more of the graph export options Torchscript Tracing and Torchscript Scripting. | +| `torch.export.save` | A model artifact storing an [ExportedProgram][exported-program] obtained by [`torch.export.export`][pytorch-export] (i.e.: `.pt2`). | +| `tf.keras.Model.save` | Saves a [.keras model file][keras-model], a unified zip archive format containing the architecture, weights, optimizer, losses, and metrics. | +| `tf.keras.Model.save_weights` | A [.weights.h5][keras-save-weights] file containing only model weights for use by Tensorflow or Keras. | +| `tf.keras.Model.export(format='tf_saved_model')` | TF Saved Model is the [recommended format][tf-keras-recommended] by the Tensorflow team for whole model saving/loading for inference. See this example to [save and load models][keras-example] and the docs for [different save methods][keras-methods] in TF and Keras. Also available from `keras.Model.export(format='tf_saved_model')` | + + +[exported-program]: https://pytorch.org/docs/main/export.html#serialization [pytorch-compile]: https://pytorch.org/tutorials/intermediate/torch_compile_tutorial.html [pytorch-export]: https://pytorch.org/docs/main/export.html [pytorch-frameworks]: https://pytorch.org/docs/main/export.html#existing-frameworks [pytorch-aot-inductor]: https://pytorch.org/docs/main/torch.compiler_aot_inductor.html [pytorch-jit-script]: https://pytorch.org/docs/stable/jit.html [pytorch-save]: https://pytorch.org/tutorials/beginner/saving_loading_models.html -[tf-save]: https://www.tensorflow.org/guide/saved_model +[keras-save-weights]: https://keras.io/api/models/model_saving_apis/weights_saving_and_loading/#save_weights-method [keras-example]: https://keras.io/guides/serialization_and_saving/ -[keras-recommended]: https://www.tensorflow.org/guide/saved_model#creating_a_savedmodel_from_keras -[h5]: https://keras.io/api/models/model_saving_apis/weights_saving_and_loading/ -[keras-methods]: https://keras.io/2.16/api/models/model_saving_apis/model_saving_and_loading/ +[tf-keras-recommended]: https://www.tensorflow.org/guide/saved_model#creating_a_savedmodel_from_keras +[keras-methods]: https://keras.io/2.16/api/models/model_saving_apis/ +[keras-model]: https://keras.io/api/models/model_saving_apis/model_saving_and_loading/ ### Source Code Asset From 2c63d5e8ba31119cdae72066c9ba72dfd240aecd Mon Sep 17 00:00:00 2001 From: Ryan Avery Date: Thu, 12 Dec 2024 16:36:17 -0800 Subject: [PATCH 73/83] move framework specific fields to best practices --- README.md | 43 ++++++------------------------------------- best-practices.md | 28 ++++++++++++++++++++++++++++ 2 files changed, 34 insertions(+), 37 deletions(-) diff --git a/README.md b/README.md index b1840a8..4b83870 100644 --- a/README.md +++ b/README.md @@ -74,7 +74,7 @@ However, fields that relate to supervised ML are optional and users can use the See [Best Practices](./best-practices.md) for guidance on what other STAC extensions you should use in conjunction -with this extension. +with this extension as well as suggested values for specific ML framework. The Machine Learning Model Extension purposely omits and delegates some definitions to other STAC extensions to favor reusability and avoid metadata duplication whenever possible. A properly defined MLM STAC Item/Collection should almost @@ -668,6 +668,7 @@ In order to provide more context, the following roles are also recommended were | type | string | The media type of the artifact (see [Model Artifact Media-Type](#model-artifact-media-type). | | roles | \[string] | **REQUIRED** Specify `mlm:model`. Can include `["mlm:weights", "mlm:checkpoint"]` as applicable. | | mlm:artifact_type | [Artifact Type](#artifact-type) | Specifies the kind of model artifact. Typically related to a particular ML framework. | +| mlm:compile_method | string | Describes the method used to compile the ML model at either save time or runtime prior to inference. These options are mutually exclusive `["aot", "jit"]`. | Recommended Asset `roles` include `mlm:weights` or `mlm:checkpoint` for model weights that need to be loaded by a model definition and `mlm:compiled` for models that can be loaded directly without an intermediate model definition. @@ -703,42 +704,10 @@ official. In order to validate the specific framework and artifact type employed [iana-media-type]: https://www.iana.org/assignments/media-types/media-types.xhtml -#### Artifact Type - -This value can be used to provide additional details about the specific model artifact being described. -For example, PyTorch offers [various strategies][pytorch-frameworks] for providing model definitions, -such as Pickle (`.pt`), [TorchScript][pytorch-jit-script], -or [PyTorch Ahead-of-Time Compilation][pytorch-aot-inductor] (`.pt2`) approach. -Since they all refer to the same ML framework, the [Model Artifact Media-Type](#model-artifact-media-type) -can be insufficient in this case to detect which strategy should be used to employ the model definition. - -Following are some proposed *Artifact Type* values for corresponding approaches, but other names are -permitted as well. Note that the names are selected using the framework-specific definitions to help -the users understand the source explicitly, although this is not strictly required either. - -| Artifact Type | Description | -|--------------------------------------------------|---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| -| `torch.save` | A [serialized python pickle object][pytorch-save] (i.e.: `.pt`) which can represent a model or state_dict. | -| `torch.jit.save` | A [`TorchScript`][pytorch-jit-script] model artifact obtained with one or more of the graph export options Torchscript Tracing and Torchscript Scripting. | -| `torch.export.save` | A model artifact storing an [ExportedProgram][exported-program] obtained by [`torch.export.export`][pytorch-export] (i.e.: `.pt2`). | -| `tf.keras.Model.save` | Saves a [.keras model file][keras-model], a unified zip archive format containing the architecture, weights, optimizer, losses, and metrics. | -| `tf.keras.Model.save_weights` | A [.weights.h5][keras-save-weights] file containing only model weights for use by Tensorflow or Keras. | -| `tf.keras.Model.export(format='tf_saved_model')` | TF Saved Model is the [recommended format][tf-keras-recommended] by the Tensorflow team for whole model saving/loading for inference. See this example to [save and load models][keras-example] and the docs for [different save methods][keras-methods] in TF and Keras. Also available from `keras.Model.export(format='tf_saved_model')` | - - - -[exported-program]: https://pytorch.org/docs/main/export.html#serialization -[pytorch-compile]: https://pytorch.org/tutorials/intermediate/torch_compile_tutorial.html -[pytorch-export]: https://pytorch.org/docs/main/export.html -[pytorch-frameworks]: https://pytorch.org/docs/main/export.html#existing-frameworks -[pytorch-aot-inductor]: https://pytorch.org/docs/main/torch.compiler_aot_inductor.html -[pytorch-jit-script]: https://pytorch.org/docs/stable/jit.html -[pytorch-save]: https://pytorch.org/tutorials/beginner/saving_loading_models.html -[keras-save-weights]: https://keras.io/api/models/model_saving_apis/weights_saving_and_loading/#save_weights-method -[keras-example]: https://keras.io/guides/serialization_and_saving/ -[tf-keras-recommended]: https://www.tensorflow.org/guide/saved_model#creating_a_savedmodel_from_keras -[keras-methods]: https://keras.io/2.16/api/models/model_saving_apis/ -[keras-model]: https://keras.io/api/models/model_saving_apis/model_saving_and_loading/ +#### Framework Specific Artifact Types + +The `mlm:artifact_type` field can be used to clarify how the model was saved which can help users understand how to load it or in what runtime contexts it should be used. For example, PyTorch offers [various strategies][pytorch-frameworks] for providing model definitions, such as Pickle (`.pt`), [TorchScript][pytorch-jit-script], or [PyTorch Ahead-of-Time Compilation][pytorch-aot-inductor] (`.pt2`) approach. Since they all refer to the same ML framework, the [Model Artifact Media-Type](#model-artifact-media-type) can be insufficient in this case to detect which strategy should be used to employ the model definition. See the [the best practices document](./best-practices#framework-specific-artifact-types) on suggested fields for framework specific artifact types. + ### Source Code Asset diff --git a/best-practices.md b/best-practices.md index d7e1675..4236db9 100644 --- a/best-practices.md +++ b/best-practices.md @@ -282,3 +282,31 @@ training process to find the "best model". This field could also be used to indi educational purposes only. [stac-ext-version]: https://github.com/stac-extensions/version + +## Framework Specific Artifact Types + +The following are some proposed *Artifact Type* values for the Model Asset's [`mlm:artifact_type` field](./README.md#model-asset). Other names are +permitted, as these values are not validated by the schema. Note that the names are selected using the framework-specific definitions to help the users understand how the model artifact was created, although these exact names are not strictly required either. + +| Artifact Type | Description | +|--------------------------------------------------|---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| +| `torch.save` | A [serialized python pickle object][pytorch-save] (i.e.: `.pt`) which can represent a model or state_dict. | +| `torch.jit.save` | A [`TorchScript`][pytorch-jit-script] model artifact obtained with one or more of the graph export options Torchscript Tracing and Torchscript Scripting. | +| `torch.export.save` | A model artifact storing an [ExportedProgram][exported-program] obtained by [`torch.export.export`][pytorch-export] (i.e.: `.pt2`). | +| `tf.keras.Model.save` | Saves a [.keras model file][keras-model], a unified zip archive format containing the architecture, weights, optimizer, losses, and metrics. | +| `tf.keras.Model.save_weights` | A [.weights.h5][keras-save-weights] file containing only model weights for use by Tensorflow or Keras. | +| `tf.keras.Model.export(format='tf_saved_model')` | TF Saved Model is the [recommended format][tf-keras-recommended] by the Tensorflow team for whole model saving/loading for inference. See this example to [save and load models][keras-example] and the docs for [different save methods][keras-methods] in TF and Keras. Also available from `keras.Model.export(format='tf_saved_model')` | + + +[exported-program]: https://pytorch.org/docs/main/export.html#serialization +[pytorch-compile]: https://pytorch.org/tutorials/intermediate/torch_compile_tutorial.html +[pytorch-export]: https://pytorch.org/docs/main/export.html +[pytorch-frameworks]: https://pytorch.org/docs/main/export.html#existing-frameworks +[pytorch-aot-inductor]: https://pytorch.org/docs/main/torch.compiler_aot_inductor.html +[pytorch-jit-script]: https://pytorch.org/docs/stable/jit.html +[pytorch-save]: https://pytorch.org/tutorials/beginner/saving_loading_models.html +[keras-save-weights]: https://keras.io/api/models/model_saving_apis/weights_saving_and_loading/#save_weights-method +[keras-example]: https://keras.io/guides/serialization_and_saving/ +[tf-keras-recommended]: https://www.tensorflow.org/guide/saved_model#creating_a_savedmodel_from_keras +[keras-methods]: https://keras.io/2.16/api/models/model_saving_apis/ +[keras-model]: https://keras.io/api/models/model_saving_apis/model_saving_and_loading/ From 3cb4e0558d228140ca142715e5a323b63f9c431f Mon Sep 17 00:00:00 2001 From: Ryan Avery Date: Thu, 12 Dec 2024 16:36:41 -0800 Subject: [PATCH 74/83] edit line length --- README.md | 7 ++++++- 1 file changed, 6 insertions(+), 1 deletion(-) diff --git a/README.md b/README.md index 4b83870..c5e2ebb 100644 --- a/README.md +++ b/README.md @@ -706,7 +706,12 @@ official. In order to validate the specific framework and artifact type employed #### Framework Specific Artifact Types -The `mlm:artifact_type` field can be used to clarify how the model was saved which can help users understand how to load it or in what runtime contexts it should be used. For example, PyTorch offers [various strategies][pytorch-frameworks] for providing model definitions, such as Pickle (`.pt`), [TorchScript][pytorch-jit-script], or [PyTorch Ahead-of-Time Compilation][pytorch-aot-inductor] (`.pt2`) approach. Since they all refer to the same ML framework, the [Model Artifact Media-Type](#model-artifact-media-type) can be insufficient in this case to detect which strategy should be used to employ the model definition. See the [the best practices document](./best-practices#framework-specific-artifact-types) on suggested fields for framework specific artifact types. +The `mlm:artifact_type` field can be used to clarify how the model was saved which can help users understand how to load it or in +what runtime contexts it should be used. For example, PyTorch offers [various strategies][pytorch-frameworks] for providing model +definitions, such as Pickle (`.pt`), [TorchScript][pytorch-jit-script], or [PyTorch Ahead-of-Time Compilation][pytorch-aot-inductor] +(`.pt2`) approach. Since they all refer to the same ML framework, the [Model Artifact Media-Type](#model-artifact-media-type) can be +insufficient in this case to detect which strategy should be used to employ the model definition. See the [the best practices +document](./best-practices#framework-specific-artifact-types) on suggested fields for framework specific artifact types. ### Source Code Asset From 4c1c63a8e3e6e117664266c4a62abed4f20a6d0d Mon Sep 17 00:00:00 2001 From: Ryan Avery Date: Thu, 12 Dec 2024 16:53:16 -0800 Subject: [PATCH 75/83] update json schema, note when the mlm:artifact type is required by mlm:model role --- README.md | 9 +++++---- json-schema/schema.json | 22 +++++++++++++++++++--- 2 files changed, 24 insertions(+), 7 deletions(-) diff --git a/README.md b/README.md index c5e2ebb..2d16b3d 100644 --- a/README.md +++ b/README.md @@ -667,8 +667,8 @@ In order to provide more context, the following roles are also recommended were | href | string | URI to the model artifact. | | type | string | The media type of the artifact (see [Model Artifact Media-Type](#model-artifact-media-type). | | roles | \[string] | **REQUIRED** Specify `mlm:model`. Can include `["mlm:weights", "mlm:checkpoint"]` as applicable. | -| mlm:artifact_type | [Artifact Type](#artifact-type) | Specifies the kind of model artifact. Typically related to a particular ML framework. | -| mlm:compile_method | string | Describes the method used to compile the ML model at either save time or runtime prior to inference. These options are mutually exclusive `["aot", "jit"]`. | +| mlm:artifact_type | [Artifact Type](#artifact-type) | Specifies the kind of model artifact. Typically related to a particular ML framework. This is **REQUIRED** if the `mlm:model` role is specified. | +| mlm:compile_method | string | Describes the method used to compile the ML model at either save time or runtime prior to inference. These options are mutually exclusive `["aot", "jit", null]`. | Recommended Asset `roles` include `mlm:weights` or `mlm:checkpoint` for model weights that need to be loaded by a model definition and `mlm:compiled` for models that can be loaded directly without an intermediate model definition. @@ -710,8 +710,9 @@ The `mlm:artifact_type` field can be used to clarify how the model was saved whi what runtime contexts it should be used. For example, PyTorch offers [various strategies][pytorch-frameworks] for providing model definitions, such as Pickle (`.pt`), [TorchScript][pytorch-jit-script], or [PyTorch Ahead-of-Time Compilation][pytorch-aot-inductor] (`.pt2`) approach. Since they all refer to the same ML framework, the [Model Artifact Media-Type](#model-artifact-media-type) can be -insufficient in this case to detect which strategy should be used to employ the model definition. See the [the best practices -document](./best-practices#framework-specific-artifact-types) on suggested fields for framework specific artifact types. +insufficient in this case to detect which strategy should be used to employ the model definition. +See the [the best practices document](./best-practices#framework-specific-artifact-types) on suggested + fields for framework specific artifact types. ### Source Code Asset diff --git a/json-schema/schema.json b/json-schema/schema.json index b87ad45..0dbf2ad 100644 --- a/json-schema/schema.json +++ b/json-schema/schema.json @@ -302,6 +302,9 @@ }, "mlm:artifact_type": { "$ref": "#/$defs/mlm:artifact_type" + }, + "mlm:compile_method": { + "$ref": "#/$defs/mlm:compile_method" } }, "$comment": "Allow properties not defined by MLM prefix to work with other extensions and attributes, but disallow undefined MLM fields.", @@ -324,7 +327,8 @@ "required": [ "mlm:input", "mlm:output", - "mlm:artifact_type" + "mlm:artifact_type", + "mlm:compile_method" ] } }, @@ -354,7 +358,8 @@ "anyOf": [ { "required": [ - "mlm:artifact_type" + "mlm:artifact_type", + "mlm:compile_method" ] } ] @@ -460,7 +465,18 @@ "examples": [ "torch.save", "torch.jit.save", - "torch.export.save" + "torch.export.save", + "tf.keras.Model.save", + "tf.keras.Model.save_weights", + "tf.saved_model.export(format='tf_saved_model')" + ] + }, + "mlm:compile_method": { + "type": "string", + "minLength": 1, + "examples": [ + "aot", + "jit" ] }, "mlm:tasks": { From 357ad61f5b3e03f64276076c8dbd518e2041820a Mon Sep 17 00:00:00 2001 From: Ryan Avery Date: Thu, 12 Dec 2024 17:14:00 -0800 Subject: [PATCH 76/83] lint fixes --- README.md | 16 ++++------------ best-practices.md | 19 ++++++++++++++----- 2 files changed, 18 insertions(+), 17 deletions(-) diff --git a/README.md b/README.md index 2d16b3d..768a021 100644 --- a/README.md +++ b/README.md @@ -667,7 +667,7 @@ In order to provide more context, the following roles are also recommended were | href | string | URI to the model artifact. | | type | string | The media type of the artifact (see [Model Artifact Media-Type](#model-artifact-media-type). | | roles | \[string] | **REQUIRED** Specify `mlm:model`. Can include `["mlm:weights", "mlm:checkpoint"]` as applicable. | -| mlm:artifact_type | [Artifact Type](#artifact-type) | Specifies the kind of model artifact. Typically related to a particular ML framework. This is **REQUIRED** if the `mlm:model` role is specified. | +| mlm:artifact_type | [Artifact Type](./best-practices.md#framework-specific-artifact-types) | Specifies the kind of model artifact. Typically related to a particular ML framework. This is **REQUIRED** if the `mlm:model` role is specified. | | mlm:compile_method | string | Describes the method used to compile the ML model at either save time or runtime prior to inference. These options are mutually exclusive `["aot", "jit", null]`. | Recommended Asset `roles` include `mlm:weights` or `mlm:checkpoint` for model weights that need to be loaded by a @@ -701,19 +701,11 @@ is used for the artifact described by the media-type. However, users need to rem official. In order to validate the specific framework and artifact type employed by the model, the MLM properties `mlm:framework` (see [MLM Fields](#item-properties-and-collection-fields)) and `mlm:artifact_type` (see [Model Asset](#model-asset)) should be employed instead to perform this validation if needed. +See the [the best practices document](./best-practices.md#framework-specific-artifact-types) on suggested +fields for framework specific artifact types. [iana-media-type]: https://www.iana.org/assignments/media-types/media-types.xhtml - -#### Framework Specific Artifact Types - -The `mlm:artifact_type` field can be used to clarify how the model was saved which can help users understand how to load it or in -what runtime contexts it should be used. For example, PyTorch offers [various strategies][pytorch-frameworks] for providing model -definitions, such as Pickle (`.pt`), [TorchScript][pytorch-jit-script], or [PyTorch Ahead-of-Time Compilation][pytorch-aot-inductor] -(`.pt2`) approach. Since they all refer to the same ML framework, the [Model Artifact Media-Type](#model-artifact-media-type) can be -insufficient in this case to detect which strategy should be used to employ the model definition. -See the [the best practices document](./best-practices#framework-specific-artifact-types) on suggested - fields for framework specific artifact types. - +[pytorch-aot-inductor]: https://pytorch.org/docs/main/torch.compiler_aot_inductor.html ### Source Code Asset diff --git a/best-practices.md b/best-practices.md index 4236db9..d778909 100644 --- a/best-practices.md +++ b/best-practices.md @@ -285,8 +285,19 @@ educational purposes only. ## Framework Specific Artifact Types -The following are some proposed *Artifact Type* values for the Model Asset's [`mlm:artifact_type` field](./README.md#model-asset). Other names are -permitted, as these values are not validated by the schema. Note that the names are selected using the framework-specific definitions to help the users understand how the model artifact was created, although these exact names are not strictly required either. +The `mlm:artifact_type` field can be used to clarify how the model was saved which +can help users understand how to load it or in what runtime contexts it should be used. For example, PyTorch offers +[various strategies][pytorch-frameworks] for providing model definitions, such as Pickle (`.pt`), + [TorchScript][pytorch-jit-script], or [PyTorch Ahead-of-Time Compilation][pytorch-aot-inductor] +(`.pt2`) approach. Since they all refer to the same ML framework, the +[Model Artifact Media-Type](./README.md#model-artifact-media-type) can be insufficient in this case to detect which +strategy should be used to employ the model definition. + +The following are some proposed *Artifact Type* values for the Model Asset's +[`mlm:artifact_type` field](./README.md#model-asset). Other names are +permitted, as these values are not validated by the schema. Note that the names are selected using the +framework-specific definitions to help the users understand how the model artifact was created, although these exact +names are not strictly required either. | Artifact Type | Description | |--------------------------------------------------|---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| @@ -297,12 +308,10 @@ permitted, as these values are not validated by the schema. Note that the names | `tf.keras.Model.save_weights` | A [.weights.h5][keras-save-weights] file containing only model weights for use by Tensorflow or Keras. | | `tf.keras.Model.export(format='tf_saved_model')` | TF Saved Model is the [recommended format][tf-keras-recommended] by the Tensorflow team for whole model saving/loading for inference. See this example to [save and load models][keras-example] and the docs for [different save methods][keras-methods] in TF and Keras. Also available from `keras.Model.export(format='tf_saved_model')` | - [exported-program]: https://pytorch.org/docs/main/export.html#serialization -[pytorch-compile]: https://pytorch.org/tutorials/intermediate/torch_compile_tutorial.html +[pytorch-aot-inductor]: https://pytorch.org/docs/main/torch.compiler_aot_inductor.html [pytorch-export]: https://pytorch.org/docs/main/export.html [pytorch-frameworks]: https://pytorch.org/docs/main/export.html#existing-frameworks -[pytorch-aot-inductor]: https://pytorch.org/docs/main/torch.compiler_aot_inductor.html [pytorch-jit-script]: https://pytorch.org/docs/stable/jit.html [pytorch-save]: https://pytorch.org/tutorials/beginner/saving_loading_models.html [keras-save-weights]: https://keras.io/api/models/model_saving_apis/weights_saving_and_loading/#save_weights-method From b54dd11694f39796024d7bc38dbfcef743fd9e9d Mon Sep 17 00:00:00 2001 From: Ryan Avery Date: Thu, 12 Dec 2024 17:48:15 -0800 Subject: [PATCH 77/83] CHANGELOG updates --- CHANGELOG.md | 41 ++++++++++------------------------------- 1 file changed, 10 insertions(+), 31 deletions(-) diff --git a/CHANGELOG.md b/CHANGELOG.md index 66b5335..fc558f9 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -5,44 +5,23 @@ All notable changes to this project will be documented in this file. The format is based on [Keep a Changelog](https://keepachangelog.com/en/1.0.0/), and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0.html). -## [Unreleased](https://github.com/stac-extensions/mlm/tree/main) +## [v1.4.0](https://github.com/stac-extensions/mlm/tree/v1.4.0) ### Added -- Add better descriptions about required and recommended *MLM Asset Roles* and their implications - (fixes [#54](https://github.com/stac-extensions/mlm/issues/54)). -- Add explicit check of `value_scaling` sub-fields `minimum`, `maximum`, `mean`, `stddev`, etc. for - corresponding `type` values `min-max` and `z-score` that depend on it. -- Allow different `value_scaling` operations per band/channel/dimension as needed by the model. -- Allow a `processing:expression` for a band/channel/dimension-specific `value_scaling` operation, - granting more flexibility in the definition of input preparation in contrast to having it applied - for the entire input (but still possible). +- mlm:compile_method with options 'aot' for Ahead of Time Compilation, 'jit' for Just-In Time Compilation ### Changed -- Explicitly disallow `mlm:name`, `mlm:input`, `mlm:output` and `mlm:hyperparameters` at the Asset level. - These fields describe the model as a whole and should therefore be defined in Item properties. -- Moved `norm_type` to `value_scaling` object to better reflect the expected operation, which could be another - operation than what is typically known as "normalization" or "standardization" techniques in machine learning. -- Moved `statistics` to `value_scaling` object to better reflect their mutual `type` and additional - properties dependencies. +- moved mlm:artifact_type field value descriptions that are framework specific to best-practices section. +- expanded suggested mlm:artifact_type values to include Tensorflow/Keras ### Deprecated - n/a ### Removed -- Removed `norm_type` enum values that were ambiguous regarding their expected result. - Instead, a `processing:expression` should be employed to explicitly define the calculation they represent. -- Removed `norm_clip` property. It is now represented under `value_scaling` objects with a - corresponding `type` definition. -- Removed `norm_by_channel` from `mlm:input` objects. If rescaling (previously normalization in the documentation) - is a single value, broadcasting to the relevant bands should be performed implicitly. - Otherwise, the amount of `value_scaling` objects should match the number of bands or channels involved in the input. +- n/a ### Fixed -- Fix missing `mlm:artifact_type` property check for a Model Asset definition - (fixes ). - The `mlm:artifact_type` is now mutually and exclusively required by the corresponding Asset with `mlm:model` role. -- Fix check of disallowed unknown/undefined `mlm:`-prefixed fields - (fixes [#41](https://github.com/stac-extensions/mlm/issues/41)). +- n/a ## [v1.3.0](https://github.com/stac-extensions/mlm/tree/v1.3.0) @@ -73,7 +52,7 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0 when a `mlm:input` references names in `bands` are now properly validated. - Fix the examples using `raster:bands` incorrectly defined in STAC Item properties. The correct use is for them to be defined under the STAC Asset using the `mlm:model` role. -- Fix the [EuroSAT ResNet pydantic example](stac_model/examples.py) that incorrectly referenced some `bands` +- Fix the [EuroSAT ResNet pydantic example](./stac_model/examples.py) that incorrectly referenced some `bands` in its `mlm:input` definition without providing any definition of those bands. The `eo:bands` properties have been added to the corresponding `model` Asset using the [`pystac.extensions.eo`](https://github.com/stac-utils/pystac/blob/main/pystac/extensions/eo.py) utilities. @@ -134,7 +113,7 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0 - more [Task Enum](README.md#task-enum) tasks - [Model Output Object](README.md#model-output-object) - batch_size and hardware summary -- [`mlm:accelerator`, `mlm:accelerator_constrained`, `mlm:accelerator_summary`](README.md#accelerator-type-enum) +- [`mlm:accelerator`, `mlm:accelerator_constrained`, `mlm:accelerator_summary`](./README.md#accelerator-type-enum) to specify hardware requirements for the model - Use common metadata [Asset Object](https://github.com/radiantearth/stac-spec/blob/master/collection-spec/collection-spec.md#asset-object) @@ -149,7 +128,7 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0 STAC Item properties (top-level, not nested) to allow better search support by STAC API. - reorganized `dlm:architecture` nested fields to exist at the top level of properties as `mlm:name`, `mlm:summary` and so on to provide STAC API search capabilities. -- replaced `normalization:mean`, etc. with [statistics](README.md#bands-and-statistics) from STAC 1.1 common metadata +- replaced `normalization:mean`, etc. with [statistics](./README.md#bands-and-statistics) from STAC 1.1 common metadata - added `pydantic` models for internal schema objects in `stac_model` package and published to PYPI - specified [rel_type](README.md#relation-types) to be `derived_from` and specify how model item or collection json should be named @@ -165,7 +144,7 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0 - any `dlm`-prefixed field or property ### Removed -- Data Object, replaced with [Model Input Object](README.md#model-input-object) that uses the `name` field from +- Data Object, replaced with [Model Input Object](./README.md#model-input-object) that uses the `name` field from the [common metadata band object][stac-bands] which also records `data_type` and `nodata` type ### Fixed From 2c3c44ec5524dcfc803e559eb21ec45a304e2678 Mon Sep 17 00:00:00 2001 From: Ryan Avery Date: Fri, 13 Dec 2024 15:02:40 -0800 Subject: [PATCH 78/83] correct the deleted unreleased items in the CHANGELOG, woops --- CHANGELOG.md | 28 ++++++++++++++++++++++++++-- 1 file changed, 26 insertions(+), 2 deletions(-) diff --git a/CHANGELOG.md b/CHANGELOG.md index fc558f9..02e0ea2 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -8,9 +8,23 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0 ## [v1.4.0](https://github.com/stac-extensions/mlm/tree/v1.4.0) ### Added +- Add better descriptions about required and recommended *MLM Asset Roles* and their implications + (fixes [#54](https://github.com/stac-extensions/mlm/issues/54)). +- Add explicit check of `value_scaling` sub-fields `minimum`, `maximum`, `mean`, `stddev`, etc. for + corresponding `type` values `min-max` and `z-score` that depend on it. +- Allow different `value_scaling` operations per band/channel/dimension as needed by the model. +- Allow a `processing:expression` for a band/channel/dimension-specific `value_scaling` operation, + granting more flexibility in the definition of input preparation in contrast to having it applied + for the entire input (but still possible). - mlm:compile_method with options 'aot' for Ahead of Time Compilation, 'jit' for Just-In Time Compilation ### Changed +- Explicitly disallow `mlm:name`, `mlm:input`, `mlm:output` and `mlm:hyperparameters` at the Asset level. + These fields describe the model as a whole and should therefore be defined in Item properties. +- Moved `norm_type` to `value_scaling` object to better reflect the expected operation, which could be another + operation than what is typically known as "normalization" or "standardization" techniques in machine learning. +- Moved `statistics` to `value_scaling` object to better reflect their mutual `type` and additional + properties dependencies. - moved mlm:artifact_type field value descriptions that are framework specific to best-practices section. - expanded suggested mlm:artifact_type values to include Tensorflow/Keras @@ -18,10 +32,20 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0 - n/a ### Removed -- n/a +- Removed `norm_type` enum values that were ambiguous regarding their expected result. + Instead, a `processing:expression` should be employed to explicitly define the calculation they represent. +- Removed `norm_clip` property. It is now represented under `value_scaling` objects with a + corresponding `type` definition. +- Removed `norm_by_channel` from `mlm:input` objects. If rescaling (previously normalization in the documentation) + is a single value, broadcasting to the relevant bands should be performed implicitly. + Otherwise, the amount of `value_scaling` objects should match the number of bands or channels involved in the input. ### Fixed -- n/a +- Fix missing `mlm:artifact_type` property check for a Model Asset definition + (fixes ). + The `mlm:artifact_type` is now mutually and exclusively required by the corresponding Asset with `mlm:model` role. +- Fix check of disallowed unknown/undefined `mlm:`-prefixed fields + (fixes [#41](https://github.com/stac-extensions/mlm/issues/41)). ## [v1.3.0](https://github.com/stac-extensions/mlm/tree/v1.3.0) From 66ca180a212f894917dbac1e780d34edae6363dd Mon Sep 17 00:00:00 2001 From: Ryan Avery Date: Fri, 13 Dec 2024 15:45:01 -0800 Subject: [PATCH 79/83] correct best practices links, be more specific in table descriptions --- README.md | 27 +++++++++++++-------- best-practices.md | 54 +++++++++++++++++++++-------------------- json-schema/schema.json | 8 ++---- 3 files changed, 47 insertions(+), 42 deletions(-) diff --git a/README.md b/README.md index 768a021..5bf4e99 100644 --- a/README.md +++ b/README.md @@ -101,7 +101,7 @@ connectors, please refer to the [STAC Model](./README_STAC_MODEL.md) document. - [Collection example](examples/collection.json): Shows the basic usage of the extension in a STAC Collection - [JSON Schema](https://stac-extensions.github.io/mlm/) - [Changelog](./CHANGELOG.md) -- [Open access paper](https://dl.acm.org/doi/10.1145/3681769.3698586) describing version 1.3.0 of the extension +- [Open access paper](https://dl.acm.org/doi/10.1145/3681769.3698586) describing version 1.3.0 of the extension - [SigSpatial 2024 GeoSearch Workshop presentation](/docs/static/sigspatial_2024_mlm.pdf) ## Item Properties and Collection Fields @@ -340,13 +340,13 @@ defined at the "Band Object" level, but at the [Model Input](#model-input-object This is because, in machine learning, it is common to need overall statistics for the dataset used to train the model to normalize all bands, rather than normalizing the values over a single product. Furthermore, statistics could be applied differently for distinct [Model Input](#model-input-object) definitions, in order to adjust for intrinsic -properties of the model. +properties of the model. Another distinction is that, depending on the model, statistics could apply to some inputs that have no reference to any `bands` definition. In such case, defining statistics under `bands` would not be possible, or would intrude ambiguous definitions. -Finally, contrary to the "`statistics`" property name employed by [Band Statistics][stac-1.1-stats], MLM employs the +Finally, contrary to the "`statistics`" property name employed by [Band Statistics][stac-1.1-stats], MLM employs the distinct name `value_scaling`, although similar `minimum`, `maximum`, etc. sub-fields are employed. This is done explicitly to disambiguate "informative" band statistics from "applied scaling operations" required by the model inputs. This highlights the fact that `value_scaling` are not *necessarily* equal @@ -449,7 +449,7 @@ Select one option from: | `scale` | `value` | $data / value$ | | `processing` | [Processing Expression](#processing-expression) | *according to `processing:expression`* | -When a scaling `type` approach is specified, it is expected that the parameters necessary +When a scaling `type` approach is specified, it is expected that the parameters necessary to perform their calculation are provided for the corresponding input dimension data. If none of the above values applies for a given dimension, `type: null` (literal `null`, not string) should be @@ -463,7 +463,7 @@ dimensions. In such case, implicit broadcasting of the unique [Value Scaling Obj performed for all applicable dimensions when running inference with the model. If a custom scaling operation, or a combination of more complex operations (with or without [Resize](#resize-enum)), -must be defined instead, a [Processing Expression](#processing-expression) reference can be specified in place of +must be defined instead, a [Processing Expression](#processing-expression) reference can be specified in place of the [Value Scaling Object](#value-scaling-object) of the respective input dimension, as shown below. ```json @@ -478,7 +478,7 @@ the [Value Scaling Object](#value-scaling-object) of the respective input dimens For operations such as L1 or L2 normalization, [Processing Expression](#processing-expression) should also be employed. This is because, depending on the [Model Input](#model-input-object) dimensions and reference data, there is an -ambiguity regarding "how" and "where" such normalization functions must be applied against the input data. +ambiguity regarding "how" and "where" such normalization functions must be applied against the input data. A custom mathematical expression should provide explicitly the data manipulation and normalization strategy. In situations of very complex `value_scaling` operations, which cannot be represented by any of the previous definition, @@ -667,8 +667,8 @@ In order to provide more context, the following roles are also recommended were | href | string | URI to the model artifact. | | type | string | The media type of the artifact (see [Model Artifact Media-Type](#model-artifact-media-type). | | roles | \[string] | **REQUIRED** Specify `mlm:model`. Can include `["mlm:weights", "mlm:checkpoint"]` as applicable. | -| mlm:artifact_type | [Artifact Type](./best-practices.md#framework-specific-artifact-types) | Specifies the kind of model artifact. Typically related to a particular ML framework. This is **REQUIRED** if the `mlm:model` role is specified. | -| mlm:compile_method | string | Describes the method used to compile the ML model at either save time or runtime prior to inference. These options are mutually exclusive `["aot", "jit", null]`. | +| mlm:artifact_type | [Artifact Type](./best-practices.md#framework-specific-artifact-types) | Specifies the kind of model artifact, any string is allowed. Typically related to a particular ML framework, see [Best Practices - Framework Specific Artifact Types](./best-practices.md#framework-specific-artifact-types) for **RECOMMENDED** values. This field is **REQUIRED** if the `mlm:model` role is specified. | +| mlm:compile_method | [Compile Method](#compile-method) | null | Describes the method used to compile the ML model either when the model is saved or at model runtime prior to inference. | Recommended Asset `roles` include `mlm:weights` or `mlm:checkpoint` for model weights that need to be loaded by a model definition and `mlm:compiled` for models that can be loaded directly without an intermediate model definition. @@ -701,12 +701,19 @@ is used for the artifact described by the media-type. However, users need to rem official. In order to validate the specific framework and artifact type employed by the model, the MLM properties `mlm:framework` (see [MLM Fields](#item-properties-and-collection-fields)) and `mlm:artifact_type` (see [Model Asset](#model-asset)) should be employed instead to perform this validation if needed. -See the [the best practices document](./best-practices.md#framework-specific-artifact-types) on suggested -fields for framework specific artifact types. +See the [Best Practices - Framework Specific Artifact Types](./best-practices.md#framework-specific-artifact-types) on + suggested fields for framework specific artifact types. [iana-media-type]: https://www.iana.org/assignments/media-types/media-types.xhtml [pytorch-aot-inductor]: https://pytorch.org/docs/main/torch.compiler_aot_inductor.html +#### Compile Method + +| Compile Method | Description | +|-:-:------------|-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| +| aot | [Ahead-of-Time Compilation](https://en.wikipedia.org/wiki/Ahead-of-time_compilation). Converts a higher level code description of a model and a model's learned weights to a lower level representation prior to executing the model. This compiled model may be more portable by having fewer runtime dependencies and optimized for specific hardware. | +| jit | [Just-in-Time Compilation](https://en.wikipedia.org/wiki/Just-in-time_compilation). Converts a higher level code description of a model and a model's learned weights to a lower level representation while executing the model. JIT provides more flexibility in the optimization approaches that can be applied to a model compared to AOT, but sacrifices portability and performance. | + ### Source Code Asset | Field Name | Type | Description | diff --git a/best-practices.md b/best-practices.md index d778909..2036523 100644 --- a/best-practices.md +++ b/best-practices.md @@ -3,18 +3,20 @@ This document makes a number of recommendations for creating real world ML Model Extensions. None of them are required to meet the core specification, but following these practices will improve the documentation of your model and make life easier for client tooling and users. They come about from practical experience of -implementors and introduce a bit more 'constraint' for those who are creating STAC objects representing their +implementors and introduce a bit more 'constraint' for those who are creating STAC objects representing their models or creating tools to work with STAC. -- [Using STAC Common Metadata Fields for the ML Model Extension](#using-stac-common-metadata-fields-for-the-ml-model-extension) -- [Recommended Extensions to Compose with the ML Model Extension](#recommended-extensions-to-compose-with-the-ml-model-extension) - - [Processing Extension](#processing-extension) - - [ML-AOI and Label Extensions](#ml-aoi-and-label-extensions) - - [Classification Extension](#classification-extension) - - [Scientific Extension](#scientific-extension) - - [File Extension](#file-extension) - - [Example Extension](#example-extension) - - [Version Extension](#version-extension) +- [ML Model Extension Best Practices](#ml-model-extension-best-practices) + - [Using STAC Common Metadata Fields for the ML Model Extension](#using-stac-common-metadata-fields-for-the-ml-model-extension) + - [Recommended Extensions to Compose with the ML Model Extension](#recommended-extensions-to-compose-with-the-ml-model-extension) + - [Processing Extension](#processing-extension) + - [ML-AOI and Label Extensions](#ml-aoi-and-label-extensions) + - [Classification Extension](#classification-extension) + - [Scientific Extension](#scientific-extension) + - [File Extension](#file-extension) + - [Example Extension](#example-extension) + - [Version Extension](#version-extension) + - [Framework Specific Artifact Types](#framework-specific-artifact-types) ## Using STAC Common Metadata Fields for the ML Model Extension @@ -68,8 +70,8 @@ information regarding these references, see the [ML-AOI and Label Extensions](#m ### Processing Extension -It is recommended to use at least the `processing:lineage` and `processing:level` fields from -the [Processing Extension](https://github.com/stac-extensions/processing) to make it clear +It is recommended to use at least the `processing:lineage` and `processing:level` fields from +the [Processing Extension](https://github.com/stac-extensions/processing) to make it clear how [Model Input Objects](./README.md#model-input-object) are processed by the data provider prior to an inference preprocessing pipeline. This can help users locate the correct version of the dataset used during model inference or help them reproduce the data processing pipeline. @@ -99,7 +101,7 @@ Furthermore, the [`processing:expression`](https://github.com/stac-extensions/pr should be specified with a reference to the STAC Item employing the MLM extension to provide full context of the source of the derived product. -A potential representation of a STAC Asset could be as follows: +A potential representation of a STAC Asset could be as follows: ```json { "model-output": { @@ -186,7 +188,7 @@ leading to a new MLM STAC Item definition (see also [STAC Version Extension](#ve ### Classification Extension -Since it is expected that a model will provide some kind of classification values as output, the +Since it is expected that a model will provide some kind of classification values as output, the [Classification Extension](https://github.com/stac-extensions/classification) can be leveraged inside MLM definition to indicate which class values can be contained in the resulting output from the model prediction. @@ -201,7 +203,7 @@ For more details, see the [Model Output Object](README.md#model-output-object) d ### Scientific Extension -Provided that most models derive from previous scientific work, it is strongly recommended to employ the +Provided that most models derive from previous scientific work, it is strongly recommended to employ the [Scientific Extension][stac-ext-sci] to provide references corresponding to the original source of the model (`sci:doi`, `sci:citation`). This can help users find more information about the model, its underlying architecture, or ways to improve it by piecing together the related work (`sci:publications`) that @@ -285,17 +287,17 @@ educational purposes only. ## Framework Specific Artifact Types -The `mlm:artifact_type` field can be used to clarify how the model was saved which -can help users understand how to load it or in what runtime contexts it should be used. For example, PyTorch offers -[various strategies][pytorch-frameworks] for providing model definitions, such as Pickle (`.pt`), - [TorchScript][pytorch-jit-script], or [PyTorch Ahead-of-Time Compilation][pytorch-aot-inductor] -(`.pt2`) approach. Since they all refer to the same ML framework, the -[Model Artifact Media-Type](./README.md#model-artifact-media-type) can be insufficient in this case to detect which -strategy should be used to employ the model definition. +The `mlm:artifact_type` field can be used to clarify how the model was saved which can help users understand how to +load it or in which runtime contexts it should be used. Applying this artifact type definition should restrict +explicitly its use to a specific runtime. For example, PyTorch offers [various strategies][pytorch-frameworks] for +exporting models, such as Pickle (`.pt`), [TorchScript][pytorch-jit-script], and +[PyTorch Ahead-of-Time Compilation][pytorch-aot-inductor] (`.pt2`). Since each approach is associated with the same +ML framework, the [Model Artifact Media-Type](./README.md#model-artifact-media-type) can be insufficient in this case +to detect which strategy should be used to deploy the model artifact. -The following are some proposed *Artifact Type* values for the Model Asset's +The following are some proposed *Artifact Type* values for the Model Asset's [`mlm:artifact_type` field](./README.md#model-asset). Other names are -permitted, as these values are not validated by the schema. Note that the names are selected using the +permitted, as these values are not validated by the schema. Note that the names are selected using the framework-specific definitions to help the users understand how the model artifact was created, although these exact names are not strictly required either. @@ -306,7 +308,7 @@ names are not strictly required either. | `torch.export.save` | A model artifact storing an [ExportedProgram][exported-program] obtained by [`torch.export.export`][pytorch-export] (i.e.: `.pt2`). | | `tf.keras.Model.save` | Saves a [.keras model file][keras-model], a unified zip archive format containing the architecture, weights, optimizer, losses, and metrics. | | `tf.keras.Model.save_weights` | A [.weights.h5][keras-save-weights] file containing only model weights for use by Tensorflow or Keras. | -| `tf.keras.Model.export(format='tf_saved_model')` | TF Saved Model is the [recommended format][tf-keras-recommended] by the Tensorflow team for whole model saving/loading for inference. See this example to [save and load models][keras-example] and the docs for [different save methods][keras-methods] in TF and Keras. Also available from `keras.Model.export(format='tf_saved_model')` | +| `tf.keras.Model.export` | [TF Saved Model][tf-saved-model] is the [recommended format][tf-keras-recommended] by the Tensorflow team for whole model saving/loading for inference. See the docs for [different save methods][keras-methods] in TF and Keras. | [exported-program]: https://pytorch.org/docs/main/export.html#serialization [pytorch-aot-inductor]: https://pytorch.org/docs/main/torch.compiler_aot_inductor.html @@ -315,7 +317,7 @@ names are not strictly required either. [pytorch-jit-script]: https://pytorch.org/docs/stable/jit.html [pytorch-save]: https://pytorch.org/tutorials/beginner/saving_loading_models.html [keras-save-weights]: https://keras.io/api/models/model_saving_apis/weights_saving_and_loading/#save_weights-method -[keras-example]: https://keras.io/guides/serialization_and_saving/ +[tf-saved-model]: https://keras.io/api/models/model_saving_apis/export/ [tf-keras-recommended]: https://www.tensorflow.org/guide/saved_model#creating_a_savedmodel_from_keras [keras-methods]: https://keras.io/2.16/api/models/model_saving_apis/ [keras-model]: https://keras.io/api/models/model_saving_apis/model_saving_and_loading/ diff --git a/json-schema/schema.json b/json-schema/schema.json index 0dbf2ad..813bb17 100644 --- a/json-schema/schema.json +++ b/json-schema/schema.json @@ -356,12 +356,8 @@ "$comment": "Particularity of the 'not/required' approach: they must be tested one by one. Otherwise, it validates that they are all (simultaneously) not present.", "not": { "anyOf": [ - { - "required": [ - "mlm:artifact_type", - "mlm:compile_method" - ] - } + {"required": ["mlm:artifact_type"]}, + {"required": ["mlm:compile_method"]} ] } }, From d770629cf2dbc2528cbaa5b6ec0129c9646c871f Mon Sep 17 00:00:00 2001 From: Ryan Avery Date: Mon, 16 Dec 2024 11:23:01 -0800 Subject: [PATCH 80/83] code ticks --- CHANGELOG.md | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/CHANGELOG.md b/CHANGELOG.md index 02e0ea2..a9d542b 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -16,7 +16,7 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0 - Allow a `processing:expression` for a band/channel/dimension-specific `value_scaling` operation, granting more flexibility in the definition of input preparation in contrast to having it applied for the entire input (but still possible). -- mlm:compile_method with options 'aot' for Ahead of Time Compilation, 'jit' for Just-In Time Compilation +- Add optional `mlm:compile_method` field at the Asset level with options `aot` for Ahead of Time Compilation, `jit` for Just-In Time Compilation. ### Changed - Explicitly disallow `mlm:name`, `mlm:input`, `mlm:output` and `mlm:hyperparameters` at the Asset level. @@ -25,8 +25,8 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0 operation than what is typically known as "normalization" or "standardization" techniques in machine learning. - Moved `statistics` to `value_scaling` object to better reflect their mutual `type` and additional properties dependencies. -- moved mlm:artifact_type field value descriptions that are framework specific to best-practices section. -- expanded suggested mlm:artifact_type values to include Tensorflow/Keras +- moved `mlm:artifact_type` field value descriptions that are framework specific to best-practices section. +- expanded suggested `mlm:artifact_type` values to include Tensorflow/Keras. ### Deprecated - n/a From 4b5898e45824e99dacc1a3e8437a04dd694cd1f9 Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Wed, 1 Jan 2025 20:55:41 +0000 Subject: [PATCH 81/83] :arrow_up: Bump actions/cache from 4.1.2 to 4.2.0 Bumps [actions/cache](https://github.com/actions/cache) from 4.1.2 to 4.2.0. - [Release notes](https://github.com/actions/cache/releases) - [Changelog](https://github.com/actions/cache/blob/main/RELEASES.md) - [Commits](https://github.com/actions/cache/compare/v4.1.2...v4.2.0) --- updated-dependencies: - dependency-name: actions/cache dependency-type: direct:production update-type: version-update:semver-minor ... Signed-off-by: dependabot[bot] --- .github/workflows/stac-model.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/workflows/stac-model.yml b/.github/workflows/stac-model.yml index 10dbe5e..25c71ca 100644 --- a/.github/workflows/stac-model.yml +++ b/.github/workflows/stac-model.yml @@ -20,7 +20,7 @@ jobs: run: make setup - name: Set up cache - uses: actions/cache@v4.1.2 + uses: actions/cache@v4.2.0 with: path: .venv key: venv-${{ matrix.python-version }}-${{ hashFiles('pyproject.toml') }}-${{ hashFiles('uv.lock') }} From 3a3d2620a5bec2363e8763e7220a1433987e259a Mon Sep 17 00:00:00 2001 From: Ryan Avery Date: Fri, 3 Jan 2025 12:52:15 -0800 Subject: [PATCH 82/83] fix line length --- CHANGELOG.md | 11 +++++++---- 1 file changed, 7 insertions(+), 4 deletions(-) diff --git a/CHANGELOG.md b/CHANGELOG.md index a9d542b..02567e1 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -8,15 +8,17 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0 ## [v1.4.0](https://github.com/stac-extensions/mlm/tree/v1.4.0) ### Added -- Add better descriptions about required and recommended *MLM Asset Roles* and their implications - (fixes [#54](https://github.com/stac-extensions/mlm/issues/54)). +- Add better descriptions about required and recommended *MLM Asset Roles* and + their implications (fixes + [#54](https://github.com/stac-extensions/mlm/issues/54)). - Add explicit check of `value_scaling` sub-fields `minimum`, `maximum`, `mean`, `stddev`, etc. for corresponding `type` values `min-max` and `z-score` that depend on it. - Allow different `value_scaling` operations per band/channel/dimension as needed by the model. - Allow a `processing:expression` for a band/channel/dimension-specific `value_scaling` operation, granting more flexibility in the definition of input preparation in contrast to having it applied for the entire input (but still possible). -- Add optional `mlm:compile_method` field at the Asset level with options `aot` for Ahead of Time Compilation, `jit` for Just-In Time Compilation. +- Add optional `mlm:compile_method` field at the Asset level with options `aot` + for Ahead of Time Compilation, `jit` for Just-In Time Compilation. ### Changed - Explicitly disallow `mlm:name`, `mlm:input`, `mlm:output` and `mlm:hyperparameters` at the Asset level. @@ -143,7 +145,8 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0 [Asset Object](https://github.com/radiantearth/stac-spec/blob/master/collection-spec/collection-spec.md#asset-object) to refer to model asset and source code. - use `classification:classes` in Model Output -- add `scene-classification` to the Enum Tasks to allow disambiguation between pixel-wise and patch-based classification +- add `scene-classification` to the Enum Tasks to allow disambiguation between + pixel-wise and patch-based classification ### Changed - `disk_size` replaced by `file:size` (see [Best Practices - File Extension](best-practices.md#file-extension)) From aee9d16e7a11c49f5f7da425a705dffb8642f258 Mon Sep 17 00:00:00 2001 From: Ryan Avery Date: Tue, 21 Jan 2025 09:56:20 -0800 Subject: [PATCH 83/83] escape pipe --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index 5bf4e99..4c99598 100644 --- a/README.md +++ b/README.md @@ -668,7 +668,7 @@ In order to provide more context, the following roles are also recommended were | type | string | The media type of the artifact (see [Model Artifact Media-Type](#model-artifact-media-type). | | roles | \[string] | **REQUIRED** Specify `mlm:model`. Can include `["mlm:weights", "mlm:checkpoint"]` as applicable. | | mlm:artifact_type | [Artifact Type](./best-practices.md#framework-specific-artifact-types) | Specifies the kind of model artifact, any string is allowed. Typically related to a particular ML framework, see [Best Practices - Framework Specific Artifact Types](./best-practices.md#framework-specific-artifact-types) for **RECOMMENDED** values. This field is **REQUIRED** if the `mlm:model` role is specified. | -| mlm:compile_method | [Compile Method](#compile-method) | null | Describes the method used to compile the ML model either when the model is saved or at model runtime prior to inference. | +| mlm:compile_method | [Compile Method](#compile-method) \| null | Describes the method used to compile the ML model either when the model is saved or at model runtime prior to inference. | Recommended Asset `roles` include `mlm:weights` or `mlm:checkpoint` for model weights that need to be loaded by a model definition and `mlm:compiled` for models that can be loaded directly without an intermediate model definition.